Face detection and alignment are two crucial tasks to face recognition which is a hot topic in the field of defense and security, whatever for the safety of social public, personal property as well as information and communication security. Common approaches toward the treatment of these tasks in recent years are often of three types: template matching-based, knowledge-based and machine learning-based, which are always separate-step, high computation cost or fragile robust. After deep analysis on a great deal of Chinese face images without hats, we propose a novel face detection and coarse alignment method, which is inspired by those three types of methods. It is multi-feature fusion with Simple Multiple Kernel Learning1 (Simple-MKL) algorithm. The proposed method is contrasted with competitive and related algorithms, and demonstrated to achieve promising results.
Sparse representation classification method has been increasingly used in the fields of computer vision and pattern analysis, due to its high recognition rate, little dependence on the features, robustness to corruption and occlusion, and etc. However, most of these existing methods aim to find the sparsest representations of the test sample y in an overcomplete dictionary, which do not particularly consider the relevant structure between the atoms in the dictionary. Moreover, sufficient training samples are always required by the sparse representation method for effective recognition. In this paper we formulate the classification as a group-structured sparse representation problem using a sparsity-inducing norm minimization optimization and propose a novel sparse representation-based automatic target recognition (ATR) framework for the practical applications in which the training samples are drawn from the simulation models of real targets. The experimental results show that the proposed approach improves the recognition rate of standard sparse models, and our system can effectively and efficiently recognize targets under real environments, especially, where the good characteristics of the sparse representation based classification method are kept.
This paper is aiming at applying sparse representation based classification (SRC) on face recognition with disguise or illumination variation. Having analyzed the characteristics of general object recognition and the principle of the classifier of SRC method, authors focus on evaluating blocks of a probe sample and propose an optimized SRC method based on position-preserving weighted block and maximum likelihood model. Principle and implementation of the proposed method have been introduced in the article, and experiments on Yale and AR face database have been given too. From experimental results, it can be seen that the proposed optimized SRC method works well than existing methods.
Texture classification is a fundamental and yet difficult task in machine vision and image processing. In recent years, more and more researchers' attention has been drawn to the sparse representation-based classification (SRC) method and its corresponding dictionaries designing in pattern recognition community, due to its high recognition rate, robustness to corruption and occlusion, and little dependence on the features, etc. In this paper, we present a discriminative dictionary learning approach, and apply it to the sparse representation based classification framework for image texture representation and classification. The experimental results conducted on different testing data demonstrate the promise of our new approach when compared with the previous algorithms.
This paper is aiming at applying sparse representation based classification (SRC) on general objects of a certain scale.
Authors analyze the characteristics of general object recognition and propose a position-weighted block dictionary
(PWBD) based on sparse presentation and design a framework of SRC with it (PWBD-SRC). Principle and
implementation of PWBD-SRC have been introduced in the article, and experiments on car models have been given in
the article. From experimental results, it can be seen that with position-weighted block dictionary (PWBD) not only the
dictionary scale can be effectively reduced, but also roles of image blocks taking in representing a whole image can be
embodied to a certain extent. In reorganization application, an image only containing partial objects can be identified
with PWBD-SRC. Besides, rotation and perspective robustness can be achieved. Finally, a brief description on some
remaining problems has been proposed in the article.