Facial Landmark Detection and Estimation under Various Expressions and Occlusions

Landmark localization is one of the fundamental approaches to facial expressions recognition, occlusions detection and face alignments. It plays a vital role in many applications in image processing and computer vision. The acquisition conditions such as expression, occlusion and background complexity affect the landmark localization performance, which subsequently lead to system failure. In this paper, the writers bestowed the challenges of various landmark detection techniques, number of landmark points and dataset types been employed from the existing literatures. However, advance technique for facial landmark detection under various expressions and occlusions was presented. This was carried out using Point Distribution Model (PDM) to estimate the occluded part of the facial regions and detect the face. The proposed method was evaluated using University Milano Bicocca Database (UMB). This approach gave more promising result when compared to several previous works. In conclusion, the technique detected images despite varieties of occlusions and expressions. It can further be applied on images with different poses and illumination variations

Gappy PCA Classification for Occlusion Tolerant 3D Face Detection

This paper presents an innovative approach for the detection of faces in three dimensional scenes. The method is tolerant against partial occlusions produced by the presence of any kind of object. The detection algorithm uses invariant properties of the surfaces to segment salient facial features, namely the eyes and the nose. At least two facial features must be clearly visible in order to perform face detection. Candidate faces are then registered using an ICP (Iterative Correspondent Point) based approach aimed to avoid those samples which belong to the occluding objects. The final face versus non-face discrimination is computed by a Gappy PCA (GPCA) classifier which is able to classify candidate faces using only those regions of the surface which are considered to be non-occluded. The algorithm has been tested using the UND database obtaining 100% of correct detection and only one false alarm. The database has been then processed with an artificial occlusions generator producing realistic acquisitions that emulate unconstrained scenarios. A rate of 89.8% of correct detections shows that 3D data is particularly suited for handling occluding objects. The results have been also verified on a small test set containing real world occlusions obtaining 90.4% of correctly detected faces. The proposed approach can be used to improve the robustness of all those systems requiring a face detection stage in non-controlled scenarios. © 2009 Springer Science+Business Media, LLC

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state