104 skills found · Page 1 of 4
serengil / DeepfaceA Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
vladmandic / HumanHuman: AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gaze Tracking, Gesture Recognition
vladmandic / Face ApiFaceAPI: AI-powered Face Detection & Rotation Tracking, Face Description & Recognition, Age & Gender & Emotion Prediction for Browser and NodeJS using TensorFlow/JS
pcb9382 / FaceAlgorithmface detection face recognition包含人脸检测(retinaface,yolov5face,yolov7face,yolov8face),人脸检测跟踪(ByteTracker)，人脸角度计算（Face_Angle）人脸矫正(Face_Aligner)，人脸识别(Arcface)，口罩检测(MaskRecognitiion)，年龄性别检测(Gender_age)，静默活体检测(Silent_Face_Anti_Spoofing),FaceAlignment(106keypoints)
sajjjadayobi / FaceLibFace Analysis: Detection, Age Gender Estimation & Recognition
dhvanikotak / Emotion Detection In VideosThe aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.
juan-csv / Face Infoface recognition, detection of facial attributes (age, gender, emotion and race) for python.
Hzzone / MTLFaceWhen Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework (CVPR 2021 oral & TPAMI 2022)
PJunhyuk / People Counting PoseOdin: Pose estimation-based tracking and counting of people in videos
KaihuaTang / ResNet50 Pytorch Face RecognitionUsing Pytorch to implement a ResNet50 for Cross-Age Face Recognition
lxq1000 / SwinFaceOfficial Pytorch Implementation of the paper, "SwinFace: A Multi-task Transformer for Face Recognition, Facial Expression Recognition, Age Estimation and Face Attribute Estimation"
kaushikjadhav01 / Deep Surveillance Monitor Facial Emotion Age Gender Recognition SystemComputer Vision module for detecting emotion, age and gender of a person in any given image, video or real time webcam. A custom VGG16 model was developed and trained on open source facial datasets downloaded from Kaggle and IMDB. OpenCV,dlib & keras were used to aid facial detection and video processing. The final system can detect the emotion, age and gender of people in any given image, video or real time webcam
weblineindia / AIML Human Attributes Detection With Facial Feature ExtractionThis is a Human Attributes Detection program with facial features extraction. It detects facial coordinates using FaceNet model and uses MXNet facial attribute extraction model for extracting 40 types of facial attributes. This solution also detects Emotion, Age and Gender along with facial attributes.
recognito-vision / NIST FRVT Top 1 Face RecognitionFace Recognition, Face Liveness Detection, Face Attribute Analysis (Age & Gender, Emotion, Demographics, Ethnicity and many more.)
Faceplugin-ltd / FaceRecognition LivenessDetection JavascriptFace Recognition SDK Javascript using ONNX Runtime Web and OpenCV.js (Face Detection, Face Landmarks, Face Liveness, Face Pose, Face Expression, Eye Closeness, Age, Gender and Face Recognition)
rileykwok / Face Recognition Model With Gender Age And Emotions EstimationsCapstone Project by Bertrand Lee and Riley Kwok
tae898 / Age GenderAge and gender recognition using arcface and MLP.
krishnaik06 / Gender Recognition And Age EstimatorNo description available
Jinnrry / FaceRecognition RestApiA Django wrapper that acts as a RESTful API for face recognition and age gender prediction
hechmik / Voxceleb Enrichment Age GenderCode and data repository for paper "VoxCeleb enrichment for Age and Gender recognition" submitted at ASRU 2021
