Facial Landmark Detection for Face Alignment and Recognition

Abstract

A crucial part of the face recognition process, along with facial attribute analysis and face verification, is facial landmark detection. In order to prepare face images for feature extraction in facial analysis jobs, face alignment is an essential step. Alignment is frequently used in training and inference to standardize the locations of important facial landmarks for applications including face recognition, facial emotion detection, and facial attribute classification. The effectiveness of facial analysis models is known to be greatly impacted by the approach and application of face alignment. However, there has not been enough research done on how alignment affects the quality of facial images. Although face alignment techniques based on convolutional neural networks have advanced significantly recently, occlusion remains a fundamental obstacle to achieving high accuracy. In order to enhance performance, we present in this study the attentioned distillation module from our earlier work on the Occlusion-Adaptive Deep Network model. A distillation module in this model infers the occlusion probability of every place in high-level features. When measuring the relationship between facial shape and look, it can be automatically learned. However, because of the missing semantic features, the holistic face cannot be represented by the clean feature representation. We must use a low-rank learning module to recover lost features in order to achieve exhaustive and complete feature representation.