Detect faces in an image and predict the emotional state of each personRecognize and extract faces in an image and complete the corrupted portions.
This model first detects faces in an input image. Then, each face is passed to the emotion classification model which predicts the emotional state of the human, from a set of 8 emotion classes: neutral, happiness, surprise, sadness, anger, disgust, fear, contempt. The output of the model is a set of bounding box coordinates and predicted probabilities for each of the emotion classes, for each face detected in the image. The format of the bounding box coordinates is [ymin, xmin, ymax, xmax], where each coordinate is normalized by the appropriate image dimension (height for y or width for x). Each coordinate is therefore in the range [0, 1].
The model is based on the Emotion FER+ ONNX Model Repo.
E. Barsoum, C Zhang, C. Canton Ferrer, Z. Zhang, “Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution”, ACM International Conference on Multimodal Interaction (ICMI), 2016.Emotion FER+ ONNX Model Repo