190 skills found · Page 1 of 7
serengil / DeepfaceA Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python
justadudewhohacks / Face Api.jsJavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
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
Faceplugin-ltd / FaceRecognition AndroidFace Recognition, Face Liveness Detection, Face Anti-Spoofing, Face Detection, Face Landmarks, Face Compare, Face Matching, Face Pose, Face Expression, Face Attributes, Face Templates Extraction, Face Landmarks
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)
primaryobjects / Voice GenderGender recognition by voice and speech analysis
recognito-vision / Linux FaceRecognition FaceLivenessDetectionNIST_FRVT Top 1🏆 Face Recognition, Liveness Detection(Face Anti-Spoof), Face Attribute Analysis Linux Server SDK Demo ☑️ Face Recognition ☑️ Face Matching ☑️ Face Liveness Detection ☑️ Face Identification (1:N Face Search) ☑️ Face Attribute Analysis
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.
SuperKogito / Voice Based Gender Recognition:sound: :boy: :girl:Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)
juan-csv / Face Infoface recognition, detection of facial attributes (age, gender, emotion and race) for python.
chenlinzhong / Gender RecognitionTensorFlow CNN卷积神经网络实现人脸性别检测
PJunhyuk / People Counting PoseOdin: Pose estimation-based tracking and counting of people in videos
diovisgood / AgenderReal-time estimation of gender and age
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.
x4nth055 / Gender Recognition By VoiceBuilding a Deep learning model that predicts the gender of a speaker using TensorFlow 2
recognito-vision / NIST FRVT Top 1 Face RecognitionFace Recognition, Face Liveness Detection, Face Attribute Analysis (Age & Gender, Emotion, Demographics, Ethnicity and many more.)
mowshon / Age And GenderPredict Age and Gender of people from images | Determination of gender and age
