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6 results for “facial database”
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 3. Feature vectors of facial expression in database
<p> Figure 3 shows feature vectors of facial expression of our database. Matrices ‘U’ and ‘V’ values that are obtained from this algorithm are used as feature vectors. The ‘U’ matrix represents the position and the ‘V’ matrix represents the change of direction. In the following, the proposed method is combined with some other feature extraction methods (LBP uniform approach and LBP circular approach) and the obtained results will be mentioned.</p>
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 1. Peak of facial expression in database
<p>In this study, data are obtained from the Kinect camera that benefits from colorful images and depth data. Kinect can record colorful and depth data simultaneously at 30 frames per second. The data are collected from the person who initially pose in front of the camera with normal face mode and then the various modes are represented. It should be noted that data are obtained at different distances from the Kinect camera and in different lighting conditions. Figure 1 shows various facial modes in our database.</p>
Image Databases for Facial Analysis Coded for Race and Gender Features
<p> </p> <p>This document consists of the corpus of image databases examined for race and gender information as published in:</p> <p><em>Morgan Klaus Scheuerman, Kandrea Wade, Caitlin Lustig, and Jed R. Brubaker. 2020. How We’ve Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis. Proc. ACM Hum.-Comput. CSCW. </em></p> <p>This code book includes:</p> <p>1. Whether race/gender is present implicitly (as descriptive, but not annotated) or explicitly (annotated/labeled). </p> <p>2. What categories or descriptions of race/gender are used.</p> <p>3. Whether those categories/descriptions use underlying source material to justify or motivate their descriptions of race/gender.</p> <p>4. Whether explicitly annotated databases describe the process of annotating race/gender.</p>
FACE-Q in Facial Reconstructive Surgery: A Prospective Database
ClinicalTrials.gov study NCT04842279. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
A novel database of Teenager's spontaneous facial expressions
<p>This dataset presents a comprehensive collection of spontaneous facial expressions obtained from 14 teenagers, encompassing six fundamental emotions: Anger, Disgust, Fear, Happy, Sad, and Surprise. With a total of 314 videos, the dataset comprises approximately 59,000 emotional frames, including both emotional expressions and neutral states. Recorded using an HD Webcam with 1080P resolution and a frame rate of 30 fps, the dataset captures authentic and unposed facial expressions, distinguishing it from existing databases. Its diverse subjects from various backgrounds enable cross-cultural and ethnic studies of facial expressions and emotions. Addressing the scarcity of similar datasets focused on teenagers, the dataset underwent thorough validation by seven validators, achieving a 64% average accuracy in emotion recognition. The dataset's potential applications range from enhancing facial expression recognition systems to investigating the impact of facial and body movements, while also providing a valuable resource for benchmarking and comparison within the vision community.</p>
Facial Genetic Syndromes Database
<p>This database contains 3544 face images of individuals with 11 genetic conditions and unaffected (without any diagnosed condition). As well as raw images and syndrome labels, we provide face bounding boxes, 5-point facial landmarks on the entire database, and Human Phenotype Ontology (HPO) annotations of 171 test images. As described in the paper, all images used in this study were identified through searches of publicly available websites and were used for noncommercial research purposes. </p> <p>We cannot share the original images and related data with requestors but have provided links to the URLs in the manuscript describing this work and snapshots in lower resolution, with the assumption that these would not be used for purposes that would not be considered fair use. These data were compiled to produce a new, derivative work, which we offer as a whole. We cannot guarantee that the URLs or images are accurate or up-to-date and encourage interested parties to refer to the sources.</p> <p>For any use of the images in the database, please respect the license and copyrights of the original images and either do not use any visual material or make sure you have acquired rights to use the visual material. </p> <p><br> <strong>Reference Conference Paper:</strong><br> Ömer Sümer, Rebekah L. Waikel, Suzanna E. Ledgister Hanchard, Dat Duong, Peter Krawitz, Cristina Conati, Benjamin D. Solomon, Elisabeth André, "Region-based Saliency Explanations on the Recognition of Facial Genetic Syndromes," Proceedings of the 8th Machine Learning for Healthcare Conference, PMLR, 2023.</p> <p><strong>Abstract:</strong><br> Deep neural networks in computer vision have shown remarkable progress in recognizing facial genetic syndromes. Many genetic syndromes are difficult to detect, even for experienced clinicians, and computer-aided phenotyping can accelerate clinical diagnosis. High-stakes clinical tasks using deep learning, as in clinical genetics, require human understandable explanations for model decisions. Saliency methods are used to explain DNN predictions in various image analysis domains but have yet to be studied in facial genetics. The syndromic features of most genetic conditions are often localized to areas like the eyes, nose, and mouth. In this paper, to summarize the contribution of key facial regions to a specific disease prediction, we propose a face region relevance score that can be applied to any saliency method. We also investigate how prior knowledge, namely human phenotype ontology and DNN model explanations, align. Quantitative experiments are performed on a new database containing over 3,500 images of 11 rare facial syndromes, a healthy control group, and an additional test set of 171 facial images, whose respective facial phenotypes are labeled by clinicians. Current saliency methods are good at capturing dysmorphism in particular regions (parts of the face), but they may not completely capture all the relevant features in a given person or condition. Our study indicates which saliency explanations and face regions are more consistent with the phenotypes of specific genetic syndromes and could be used in large-scale clinical evaluations.</p>
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OpenNeuro
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