A Multi-Stage Classifier for Face Recognition Undertaken by Coarse-to-fine Strategy

Face recognition has been a very active research area for past two decades due to its widely applications such as identity authentication, airport security and access control, surveillance, and video retrieval systems, etc. Numerous approaches have been proposed for face recognition and considerable successes have been reported [1]. A successful face recognitio

Face Image Retrieval in Image Processing – A Survey

The task of face recognition has been actively researched in recent years. Face recognition has been a challenging and interesting area in real time applications. With the exponentially growing images, large-scale content-based face image retrieval is an enabling technology for many emerging applications. A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. Here first we present an overview of face recognition and discuss the methodology and its functioning. Thereafter we represent the most recent face recognition techniques listing their advantages and disadvantages. Some techniques specified here also improve the efficiency of face recognition under various illumination and expression condition of face images This include PCA, LDA, SVM, Gabor wavelet soft computing tool like ANN for recognition and various hybrid combination of these techniques. This review investigates all these methods with parameters that challenges face recognition like illumination, pose variation, facial expressions. This paper also focuses on related work done in the area of face image retrieval

A generic face processing framework: technologies, analyses and applications.

Jang Kim-fung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 108-124).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Introduction about Face Processing Framework --- p.4Chapter 1.2.1 --- Basic architecture --- p.4Chapter 1.2.2 --- Face detection --- p.5Chapter 1.2.3 --- Face tracking --- p.6Chapter 1.2.4 --- Face recognition --- p.6Chapter 1.3 --- The scope and contributions of the thesis --- p.7Chapter 1.4 --- The outline of the thesis --- p.8Chapter 2 --- Facial Feature Representation --- p.10Chapter 2.1 --- Facial feature analysis --- p.10Chapter 2.1.1 --- Pixel information --- p.11Chapter 2.1.2 --- Geometry information --- p.13Chapter 2.2 --- Extracting and coding of facial feature --- p.14Chapter 2.2.1 --- Face recognition --- p.15Chapter 2.2.2 --- Facial expression classification --- p.38Chapter 2.2.3 --- Other related work --- p.44Chapter 2.3 --- Discussion about facial feature --- p.48Chapter 2.3.1 --- Performance evaluation for face recognition --- p.49Chapter 2.3.2 --- Evolution of the face recognition --- p.52Chapter 2.3.3 --- Evaluation of two state-of-the-art face recog- nition methods --- p.53Chapter 2.4 --- Problem for current situation --- p.58Chapter 3 --- Face Detection Algorithms and Committee Ma- chine --- p.61Chapter 3.1 --- Introduction about face detection --- p.62Chapter 3.2 --- Face Detection Committee Machine --- p.64Chapter 3.2.1 --- Review of three approaches for committee machine --- p.65Chapter 3.2.2 --- The approach of FDCM --- p.68Chapter 3.3 --- Evaluation --- p.70Chapter 4 --- Facial Feature Localization --- p.73Chapter 4.1 --- Algorithm for gray-scale image: template match- ing and separability filter --- p.73Chapter 4.1.1 --- Position of face and eye region --- p.74Chapter 4.1.2 --- Position of irises --- p.75Chapter 4.1.3 --- Position of lip --- p.79Chapter 4.2 --- Algorithm for color image: eyemap and separa- bility filter --- p.81Chapter 4.2.1 --- Position of eye candidates --- p.81Chapter 4.2.2 --- Position of mouth candidates --- p.83Chapter 4.2.3 --- Selection of face candidates by cost function --- p.84Chapter 4.3 --- Evaluation --- p.85Chapter 4.3.1 --- Algorithm for gray-scale image --- p.86Chapter 4.3.2 --- Algorithm for color image --- p.88Chapter 5 --- Face Processing System --- p.92Chapter 5.1 --- System architecture and limitations --- p.92Chapter 5.2 --- Pre-processing module --- p.93Chapter 5.2.1 --- Ellipse color model --- p.94Chapter 5.3 --- Face detection module --- p.96Chapter 5.3.1 --- Choosing the classifier --- p.96Chapter 5.3.2 --- Verifying the candidate region --- p.97Chapter 5.4 --- Face tracking module --- p.99Chapter 5.4.1 --- Condensation algorithm --- p.99Chapter 5.4.2 --- Tracking the region using Hue color model --- p.101Chapter 5.5 --- Face recognition module --- p.102Chapter 5.5.1 --- Normalization --- p.102Chapter 5.5.2 --- Recognition --- p.103Chapter 5.6 --- Applications --- p.104Chapter 6 --- Conclusion --- p.106Bibliography --- p.10

Face recognition enhancement through the use of depth maps and deep learning

Face recognition, although being a popular area of research for over a decade has still many open research challenges. Some of these challenges include the recognition of poorly illuminated faces, recognition under pose variations and also the challenge of capturing sufficient training data to enable recognition under pose/viewpoint changes. With the appearance of cheap and effective multimodal image capture hardware, such as the Microsoft Kinect device, new possibilities of research have been uncovered. One opportunity is to explore the potential use of the depth maps generated by the Kinect as an additional data source to recognize human faces under low levels of scene illumination, and to generate new images through creating a 3D model using the depth maps and visible-spectrum / RGB images that can then be used to enhance face recognition accuracy by improving the training phase of a classification task.. With the goal of enhancing face recognition, this research first investigated how depth maps, since not affected by illumination, can improve face recognition, if algorithms traditionally used in face recognition were used. To this effect a number of popular benchmark face recognition algorithms are tested. It is proved that algorithms based on LBP and Eigenfaces are able to provide high level of accuracy in face recognition due to the significantly high resolution of the depth map images generated by the latest version of the Kinect device. To complement this work a novel algorithm named the Dense Feature Detector is presented and is proven to be effective in face recognition using depth map images, in particular under wellilluminated conditions. Another technique that was presented for the goal of enhancing face recognition is to be able to reconstruct face images in different angles, through the use of the data of one frontal RGB image and the corresponding depth map captured by the Kinect, using faster and effective 3D object reconstruction technique. Using the Overfeat network based on Convolutional Neural Networks for feature extraction and a SVM for classification it is shown that a technically unlimited number of multiple views can be created from the proposed 3D model that consists features of the face if captured real at similar angles. Thus these images can be used as real training images, thus removing the need to capture many examples of a facial image from different viewpoints for the training of the image classifier. Thus the proposed 3D model will save significant amount of time and effort in capturing sufficient training data that is essential in recognition of the human face under variations of pose/viewpoint. The thesis argues that the same approach can also be used as a novel approach to face recognition, which promises significantly high levels of face recognition accuracy base on depth images. Finally following the recent trends in replacing traditional face recognition algorithms with the effective use of deep learning networks, the thesis investigates the use of four popular networks, VGG-16, VGG-19, VGG-S and GoogLeNet in depth maps based face recognition and proposes the effective use of Transfer Learning to enhance the performance of such Deep Learning networks

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

Robust face recognition via accurate face alignment and sparse representation

Due to its potential applications, face recognition has been receiving more and more research attention recently. In this paper, we present a robust real-time facial recognition system. The system comprises three functional components, which are face detection, eye alignment and face recognition, respectively. Within the context of computer vision, there are lots of candidate algorithms to accomplish the above tasks. Having compared the performance of a few state-of-the-art candidates, robust and efficient algorithms are implemented. As for face detection, we have proposed a new approach termed Boosted Greedy Sparse Linear Discriminant Analysis (BGSLDA) that produces better performances than most reported face detectors. Since face misalignment significantly deteriorates the recognition accuracy, we advocate a new cascade framework including two different methods for eye detection and face alignment. We have adopted a recent algorithm termed Sparse Representation-based Classification (SRC) for the face recognition component. Experiments demonstrate that the whole system is highly qualified for efficiency as well as accuracy.Hanxi Li, Peng Wang and Chunhua Shenhttp://dicta2010.conference.nicta.com.au