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BP4D-Spontanous Database. Well-validated emotion inductions were used to elicit expressions of emotion and paralinguistic communication. Frame-level ground-truth for facial actions was obtained using the Facial Action Coding System.
Facial features were tracked in both 2D and 3D domains using both person-specific and generic approaches. The work promotes the exploration of 3D spatiotemporal features in subtle facial expression, better understanding of the relation between pose and motion dynamics in facial action units, and deeper understanding of naturally occurring facial action. The database includes 41 participants 23 women, 18 men. An emotion elicitation protocol was designed to elicit emotions of participants effectively.
Eight tasks were covered with an interview process and a series of activities to elicit eight emotions. The database is structured by participants. Each participant is associated with 8 tasks. For each task, there are both 3D and 2D videos. 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.
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