The database is in the size of about 2. The database contains 3D face and hand scans. It was acquired using the structured light technology. According to our knowledge it is the first publicly available database where both sides of a hand were captured within one scan.
Although there is a large amount of research examining the perception of emotional facial expressions, almost all of this research has focused on the perception of adult facial expressions. There are several excellent stimulus sets of adult facial expressions that can be easily obtained and used in scientific research i.
However, there is no complete stimulus set of child affective facial expressions, and thus research on the perception of children making affective facial expression is sparse. In order to fully understand how humans respond to and process affective facial expressions, it is important to have this understanding across a variety of means. The Child Affective Facial Expressions Set CAFE is the first attempt to create a large and representative set of children making a variety of affective facial expressions that can be used for scientific research in this area.
The set is made up of photographs of over child models ages making 7 different facial expressions - happy, angry, sad, fearful, surprise, neutral, and disgust. It is mainly intended to be used for benchmarking of the face identification methods, however it is possible to use this corpus in many related tasks e. Two different partitions of the database are available. The first one contains the cropped faces that were automatically extracted from the photographs using the Viola-Jones algorithm.
The face size is thus almost uniform and the images contain just a small portion of background. The images in the second partition have more background, the face size also significantly differs and the faces are not localized. The purpose of this set is to evaluate and compare complete face recognition systems where the face detection and extraction is included. Each photograph is annotated with the name of a person. There are facial images for 13 IRTT students.
They are of same age factor around 23 to 24 years. The images along with background are captured by canon digital camera of The actual size of cropped faces x and they are further resized to downscale factor 5.
Out of 13, 12 male and one female. Each subject have variety of face expressions, little makeup, scarf, poses and hat also. The database version 1. There are facial images for 10 IRTT girl students all are female with 10 faces per subject with age factor around 23 to 24 years. The colour images along with background are captured with a pixel resolution of x and their faces are cropped to x pixels.
This IRTT student video database contains one video in. Later more videos will be included in this database. The video duration is This video is captured by smart phone. The faces and other features like eyes, lips and nose are extracted from this video separately. Part one is a set of color photographs that include a total of faces in the original format given by our digital cameras, offering a wide range of difference in orientation, pose, environment, illumination, facial expression and race.
Part two contains the same set in a different file format. The third part is a set of corresponding image files that contain human colored skin regions resulting from a manual segmentation procedure. The fourth part of the database has the same regions converted into grayscale. The database is available on-line for noncommercial use. The database is designed for providing high-quality HD multi-subject banchmarked video inputs for face recognition algorithms.
The database is a useful input for offline as well as online Real-Time Video scenarios. It is harvested from Google image search. The dataset contains annotated cartoon faces of famous personalities of the world with varying profession. Additionally, we also provide real faces of the public figure to study cross modal retrieval tasks, such as, Photo2Cartoon retrieval.
The IIIT-CFW can be used for the study spectrum of problems, such as, face synthesis, heterogeneous face recognition, cross modal retrieval, etc.
Please use this database only for the academic research purpose. The database contains multiple face images of six stylized characters. The database contains facial expression images of six stylized characters.
The images for each character is grouped into seven types of expressions - anger, disgust, fear, joy, neutral, sadness and surprise. The dataset contains 3, images of 1, celebrities. Specs on Faces SoF Dataset. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is devoted to two problems that affect face detection, recognition, and classification, which are harsh illumination environments and face occlusions.
The glasses are the common natural occlusion in all images of the dataset. However, the glasses are not the sole facial occlusion in the dataset; there are two synthetic occlusions nose and mouth added to each image.
Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty easy, medium, and hard. That enlarges the number of images to be 42, images 26, male images and 16, female images. Furthermore, the dataset comes with a metadata that describes each subject from different aspects.
The original images without filters or synthetic occlusions were captured in different countries over a long period. The data set is unrestricted, as such, it contains large pose, lighting, expression, race and age variation. It also contains images which are artistic impressions drawings, paintings etc. All images have size x pixels and are stored with jpeg compression. To simulate multiple scenarios, the images are captured with several facial variations, covering a range of emotions, actions, poses, illuminations, and occlusions.
The database includes the raw light field images, 2D rendered images and associated depth maps, along with a rich set of metadata. Each subject is attempting to spoof a target identity. Hence this dataset consists of three sets of face images: images of a subject before makeup; images of the same subject after makeup with the intention of spoofing; and images of the target subject who is being spoofed. The database is gender balanced consisting of 24 professional actors, vocalizing lexically-matched statements in a neutral North American accent.
Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity, with an additional neutral expression.
All conditions are available in face-and-voice, face-only, and voice-only formats. The set of recordings were rated by adult participants. High levels of emotional validity and test-retest intrarater reliability were reported, as described in our PLoS One paper. All recordings are made freely available under a Creative Commons license, non-commerical license. Disguised Faces in the Wild. Face recognition research community has prepared several large-scale datasets captured in uncontrolled scenarios for performing face recognition.
However, none of these focus on the specific challenge of face recognition under the disguise covariate. The proposed DFW dataset consists of 11, images of 1, subjects. The dataset contains a broad set of unconstrained disguised faces, taken from the Internet. The dataset encompasses several disguise variations with respect to hairstyles, beard, mustache, glasses, make-up, caps, hats, turbans, veils, masquerades and ball masks.
This is coupled with other variations with respect to pose, lighting, expression, background, ethnicity, age, gender, clothing, hairstyles, and camera quality, thereby making the dataset challenging for the task of face recognition. The paper describing the database and the protocols is available here. In affective computing applications, access to labeled spontaneous affective data is essential for testing the designed algorithms under naturalistic and challenging conditions.
Most databases available today are acted or do not contain audio data. BAUM-1 is a spontaneous audio-visual affective face database of affective and mental states. The video clips in the database are obtained by recording the subjects from the frontal view using a stereo camera and from the half-profile view using a mono camera. The subjects are first shown a sequence of images and short video clips, which are not only meticulously fashioned but also timed to evoke a set of emotions and mental states.
Then, they express their ideas and feelings about the images and video clips they have watched in an unscripted and unguided way in Turkish. The target emotions, include the six basic ones happiness, anger, sadness, disgust, fear, surprise as well as boredom and contempt.
We also target several mental states, which are unsure including confused, undecided , thinking, concentrating, and bothered. Baseline experimental results on the BAUM-1 database show that recognition of affective and mental states under naturalistic conditions is quite challenging.
The database is expected to enable further research on audio-visual affect and mental state recognition under close-to-real scenarios.
NMAPS is a database of human face images and their corresponding sketches generated using a novel approach implemented using Matlab tool.
Images were taken under the random lighting conditions and environment with varying background and quality. Images captured under the varying conditions and quality mimic the real-world conditions and enables the researches to try out robust algorithms testing in the area of sketch generation and matching. This database is an unique contribution in the field of forensic science research as it contains the photo-sketch data-sets of South Indian people.
The database was collected from 50 subjects of different age, sex and ethnicity, resulting a total of images. Variations include Expression, Pose, Occlusion and Illumination.
The images include the frontal pose of the subjects. Co-variates include illumination, expression, image quality and resolution. Further challenging in this dataset are beautification e. We obtained annotations related to te subjects' body weight and height from websites such as www. Human emotion recognition is of par importance for human computer interaction. Dataset and it's quality plays important role in this domain.
The dataset contains clips of 44 volunteers between 17 to 22 year of age. All the clips are manually splitted from the video recorded during stimulent clips are watched by volunteers. Facial expressions are self annotated by the volunteers as well as cross annotated by annotators. Analysis of the dataset is done using Resnet34 neural network and baseline for the dataset is provided for research and comparison. The dataset is described in this paper. Grammatical Facial Expressions Data Set.
The automated analysis of facial expressions has been widely used in different research areas, such as biometrics or emotional analysis. Special importance is attached to facial expressions in the area of sign language, since they help to form the grammatical structure of the language and allow for the creation of language disambiguation, and thus are called Grammatical Facial Expressions.
This dataset was already used in the experiments described in Freitas et al. The dataset is composed by eighteen videos recorded using Microsoft Kinect sensor. In each video, a user performs five times , in front of the sensor, five sentences in Libras Brazilian Sign Language that require the use of a grammatical facial expression.
By using Microsoft Kinect, we have obtained: a a image of each frame, identified by a timestamp; b a text file containing one hundred coordinates x, y, z of points from eyes, nose, eyebrows, face contour and iris; each line in the file corresponds to points extracted from one frame.
The images enabled a manual labeling of each file by a specialist, providing a ground truth for classification. The dataset is organized in 36 files: 18 datapoint files and 18 target files, one pair for each video which compose the dataset.
The name of the file refers to each video: the letter corresponding to the user A and B , name of grammatical facial expression and a specification target or datapoints. The database contains images in visible, infrared, visible-plus-infrared, and thermal modalities. A total of subjects, 60 male and 40 female, with various facial disguise add-ons. The database contains images with natural face, real beard, cap, scarf, glasses, mask, makeup, wig, fake beard, fake mustache, and their variations.
Each facial addon is captured with multiple variations in pose. The database contains a total of 24, facial images. For further information about Sejong Face Database see here. Faces form the basis for a rich variety of judgments in humans, yet the underlying features remain poorly understood. Fine-grained distinctions within a race might more strongly constrain the possible features but are relatively less studied. Fine-grained race classification is also interesting because even humans may not be perfectly accurate on these tasks.
This offers a unique opportunity to compare errors made by humans and machines, in contrast to standard object detection tasks where human performance is nearly perfect. We have benchmarked North Vs South categorization on close to humans and machines performance on a novel database of close to diverse Indian faces labeled for fine-grained race South vs North India as well as for age, weight, height, and gender.
General Info New face-rec. Read more: R. SCfaceDB Landmarks The database is comprised of 21 facial landmarks from face images from users annotated manually by a human operator, as described in this paper. Besides central point, 3 temperature measuring points can be added.
Display resolution. PC analysis software PC. Data communication. Type-C USB. Auto power off. Selectable 5min, 10min, 30min , 30min auto power off default. Service time. Charging time. Image storage. Micro SD card. General Characteristics. A single Li-ion 3. Product color. Product size. Standard accessories. Standard carton gross weight.
Iron red, rainbow, white, black, red, lava, high-contrast rainbow. Thermal imagery, digital camera visible light , fusion. Visible light. Resolution of visible light. Mixed setting. Real-time image transmission. Yes PC software image projection. Thermal sensitivity. Measuring mode. Infrared focal plane measuring temperature. Measuring accuracy. Focus mode. Wavelength range.
Image frequency. Rainbow, black and white. Battery type. Operating temperature. Storage temperature. Drop resistance. Weight: g. Infrared resolution. Detector resolution. Field of view. Minimum focus distance. Adjustable 0. Temperature measurement range. Spectral band. Focus system. Fixed focus. Color palettes.
Ironbow, Iridescence, Iridescence high contrast, Grayscale, Grayscale inverted. View options. Image file formats. Alkaline battery AA 1. Battery life. Automatic shutdown time. Operating temperature and humidity. Storage temperature and humidity. Specification: Item. Share on twitter. Share on linkedin. Share on pinterest. Share on print. Share on email.
Search for:. Start a Zoom meeting. In this way, the techniques described herein can enhance the use of information that is often lost or inaccessible to employees of a company. Decisions may be made faster and may be better informed than previously possible. Accordingly, companies may become more productive. Another exemplary use case further illustrates the utility of the described techniques.
For example, a tabletop device is made available in a classroom. The classroom has 20 students and an electronic device for each of the students. The electronic devices are in electronic communication with the tabletop device. A teacher triggers distribution of an original source document to each of the electronic devices. The original source document includes a passage of literature. The teacher requests that each of the students make annotations on the original source document as to any portion of the passage that has an error and as to any portion of the passage that they particularly find significant or enjoyable.
During the exercise, each of the students reads his respective copy of the source document and makes annotations. The teacher, perhaps through the tabletop device at his desk, watches in approximate real-time the progress of the students. The teacher could identify any student in approximate real-time who needs attention. At a desired time, the teacher triggers retrieval and aggregation of annotations from all students.
The devices, the tabletop device or combination thereof perform a semantic analysis of the annotations made by the students. The teacher then triggers the tabletop device to display a color coded or other version of an annotated original document on the whiteboard for further discussion in the class. The version displayed may include some or all of the annotations made by the students, or may include an interpretation or aggregation and synthesis of the annotations made by the students.
Further, the annotations from the 20 students may be combined with annotations made by other classes at the same school but perhaps in previous years or may be combined with annotations made by other classes across the country.
An annotated resulting document may be considered a social map that composites information from all or a plurality of the students and the annotations may be broken down by sex of the student, previously measured reading ability of the student, etc. In this use case, document journaling provides an enhanced learning experience for the students. Further, document journaling may record participation of each of the students for each activity during a day, and for each day throughout an academic year.
Participation, annotations, timestamps, etc. Further, participation, annotations, timestamps, etc. The techniques described in reference to this use case can enhance the use of information that is often lost or inaccessible to teachers, administrators and students in a pedagogical setting. Acquiring an image may be done through one or more mechanisms.
For example, a version of a document may be annotated electronically on a tablet computing device and transferred to or shared with another network accessible computer. For another example, a paper version of a document may be annotated with an ink pen, scanned by a sheet feeding scanning device and transferred to or shared by the sheet feeding scanning device to a network accessible computer. After an image of a document is acquired, the document and environment are analyzed step Analysis of the document includes separating annotations and any recordings made during an annotation session from the original content of the document.
Analysis of the document also includes a semantic evaluation of the original content of the document, step Analysis includes a semantic evaluation of any annotations on the annotated document and a semantic analysis or evaluation of sound, video or other recordings made contemporaneously during an annotation session, step Voice or other audio recordings made during annotation of a document may be transcribed, and the transcription is semantically analyzed.
Further, analysis of the document includes identification of any connective elements such as marks that connect annotations with particular portions of the document, step A handwritten arrow such as that shown along with note 2 in FIG. After analysis, associations are made between the original content of the document and annotations, etc. Associations may be made, for example, between a first portion of the document and other portions of the content of the document, between a first portion of the document and an annotation step , between a first portion of the document and a connective element, between a first portion of the document and a portion of a transcription of a recording made contemporaneously during an annotation session step , and between annotations, connective elements and portions of transcriptions of recordings.
Once one or more associations are created, document content, annotations and associations are stored, step Step may include associating a timestamp, time, date or other time-relevant parameter with each portion of content of the document, such as step The timestamp of a document may be a time and date of creation of the original document, a time and date of last modification of the original document, a time and date of scanning of the annotated document, a time and date of sharing of the original document such as at a meeting or by sending to annotators , etc.
Further, step may include associating a timestamp, time, date or other time-relevant parameter with each annotation, audio recording, video recording, picture recording, etc. Step may also include associating a location or information associated with a location with each annotation, audio recording, video recording, picture recording, etc. Referring to FIG. The processor may represent one or more processors e. In addition, the memory may be considered to include memory storage physically located elsewhere in the hardware , e.
The hardware also typically receives a number of inputs and outputs for communicating information externally. For interface with a user or operator, the hardware may include one or more user input devices e. For additional storage, the hardware may also include one or more mass storage devices , e.
Furthermore, the hardware may include an interface with one or more networks e. The hardware operates under the control of an operating system , and executes various computer software applications, components, programs, objects, modules, etc. Moreover, various applications, components, programs, objects, etc.
Moreover, while the invention has been described in the context of fully functioning computers and computer systems, those skilled in the art will appreciate that the various embodiments of the invention are capable of being distributed as a program product in a variety of forms, and that the invention applies equally regardless of the particular type of computer-readable media used to actually effect the distribution.
Examples of computer-readable media include but are not limited to recordable type media such as volatile and non-volatile memory devices, floppy and other removable disks, hard disk drives, optical disks e. Another type of distribution may be implemented as Internet downloads. While certain exemplary embodiments and implementations have been described and shown in the accompanying drawings, it is to be understood that such embodiments and implementations are merely illustrative and not restrictive of the broad invention and that this invention is not limited to the specific constructions and arrangements shown and described, since various other modifications may occur to those ordinarily skilled in the art upon studying this disclosure.
In an area of technology such as this, where growth is fast and further advancements are not easily foreseen, the disclosed embodiments may be readily modifiable in arrangement and detail as facilitated by enabling technological advancements without departing from the principals of the present disclosure. A method in a computer system for finding and presenting information, the method comprising: acquiring an image of a document having original information and one or more annotations;.
The method of claim 1 , wherein performing the semantic evaluation includes: identifying a relationship search query;. The method of claim 1 , wherein the method further comprises: encrypting the isolated annotations; and. The method of claim 1 , wherein the method further comprises: identifying a user associated with the isolated annotations;. The method of claim 1 , wherein the method further comprises: acquiring an image of a second copy of the document having printed information;.
The method of claim 1 , wherein the document includes a document identifier, and wherein the method further comprises: recognizing the document identifier; and. The method of claim 1 , wherein the method further comprises: retrieving from the document a geolocation identifier for the document; and. The method of claim 1 , wherein creating the association between the portion of the isolated annotations and one or more portions of the original information is based at least in part upon a location of an annotation relative to the one or more portions of the original information.
A device for acquiring related information from a corpus of documents, the device comprising: an optical sensor;. The device of claim 9 , wherein performing the semantic search includes: determining a set of data objects from the at least one of the portion of the annotation text and the portion of the document information; and.
The device of claim 9 , wherein acquiring with the optical sensor the image of the document with the optical sensor includes acquiring a semi-unique document identifier, wherein the semi-unique identifier includes information about an identity of a person associated with the annotation. The device of claim 9 , wherein acquiring with the optical sensor the image of the document having document information and annotation includes retrieving a portion of geolocation information, associating associating the portion of geolocation information with the image of the document and with a timestamp associated with the acquiring with the optical sensor the image of the document.
One or more physical computer-accessible media encoded with instructions for performing a method, the method comprising: acquiring an image of a document that includes document information and an annotation;. The one or more physical computer-accessible media of claim 13 , wherein the acquiring the image of the document includes performing an optical scan of a paper version of a document. The one or more physical computer-accessible media of claim 13 , wherein the method further comprises: encrypting the isolated annotation; and.
The one or more physical computer-accessible media of claim 13 , wherein the method further comprises: acquiring an image of a second copy of the document having document information;.
The one or more physical computer-accessible media of claim 13 , wherein the document includes a document identifier, wherein the acquiring the image of the document includes acquiring a copy of the document identifier, and wherein the method further comprises: recognizing the document identifier; and. The one or more physical computer-accessible media of claim 13 , wherein the method further comprises: retrieving from the document a geolocation identifier for the document; and.
The one or more physical computer-accessible media of claim 13 , wherein performing a semantic evaluation includes identifying a grammatical relationship between a first entity and at least one of a second entity or an action.
The one or more physical computer-accessible media of claim 13 , wherein 1 isolating the annotation added to the document, 2 performing the semantic evaluation of the isolated annotation, 3 performing the semantic evaluation of the document information, and 4 creating the association between the portion of the isolated annotation and one or more portions of the document information, are performed in approximate real-time as the annotation is made to the document.
USB2 en. Information browsing apparatus and recording medium for computer to read, storing computer program. Method for sharing notes of an electronic book and electronic reader thereof, computer readable storage medium. Creation and exposure of embedded secondary content data relevant to a primary content page of an electronic book.
USB1 en. Framework for continuous processing of a set of documents by multiple software applications. Systems and methods for categorizing, evaluating, and displaying user input with publishing content. Method and apparatus of processing user data of a multi-speaker conference call. Information processing system, electronic apparatus, information processing apparatus, information processing method, electronic apparatus processing method and non-transitory computer readable medium.
Method of providing service for user search, and apparatus, server, and system for the same.
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