Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib. Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level. The easiest way to install deepface is to download it from PyPI. It's going to install the library itself and its prerequisites as well.

Highlights: Face detection and alignment are correlated problems. Change in various poses, illuminations, and occlusions in unrestrained environments can make these problems even more challenging. In this tutorial, we will study how deep learning approaches can be great performing solutions for these two problems. We will study a deep cascaded multi-task framework proposed by Kaipeng Zhang [1] et al. that predicts face and landmark location in a coarse-to-fine manner. Recognizing faces and expressions involves crucial face detection and alignment solutions.

Abstract-- This paper describes audEERING's submissions as well as additional evaluations for the One-Minute-Gradual (OMG) emotion recognition challenge. We provide the results for audio and video processing on subject (in)dependent evaluations. On the provided Development set, we achieved 0.343 Concordance Correlation Coefficient (CCC) for arousal (from audio) and.401 for valence (from video). I. INTRODUCTION The OMG dataset consists of 5288 (train: 2442, dev: 617, test: 2229) segments from YouTube videos of about 1-minute each, and the raters annotated some segments in each video on arousal (activation) [0..1] and valence [-1..1] dimensions. For the sake of consistency we mapped arousal also to [-1..1] range.