K. Horadam

Bio: K. Horadam is an academic researcher from Melbourne Institute of Technology. The author has contributed to research in topics: Image registration & Facial recognition system. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

Robust Shape-Feature-Vector-Based Face Recognition System

Abstract: This paper presents a feature-based approach for fast face recognition. A novel shape-based automatic reference control point and feature extraction technique is proposed for face representation, whereby the difference between two faces is measured by a set of extracted features, and 3-D features from a set of 2-D images are used for face template registration. Unlike holistic face recognition algorithms, the feature-based algorithm is relatively robust to variations of face expressions, illumination, and pose, due to invariance of its facial feature vector. The theoretical performance analysis of the proposed technique was provided by a probabilistic and statistical approach. The proposed approach is shown to achieve promising performance for face recognition using several subsets of face recognition databases.

A review of face recognition methods

Abstract: Face recognition has become more significant and relevant in recent years owing to it potential applications. Since the faces are highly dynamic and pose more issues and challenges to solve, researchers in the domain of pattern recognition, computer vision and artificial intelligence have proposed many solutions to reduce such difficulties so as to improve the robustness and recognition accuracy. As many approaches have been proposed, efforts are also put in to provide an extensive survey of the methods developed over the years. The objective of this paper is to provide a survey of face recognition papers that appeared in the literature over the past decade under all severe conditions that were not discussed in the previous survey and to categorize them into meaningful approaches, viz. appearance based, feature based and soft computing based. A comparative study of merits and demerits of these approaches have been presented.

Face Recognition by Exploiting Local Gabor Features With Multitask Adaptive Sparse Representation

Abstract: For a human face, the Gabor transform can extract its multiple scale and orientation features that are very useful for the recognition. In this paper, the Gabor-feature-based face recognition is formulated as a multitask sparse representation model, in which the sparse coding of each Gabor feature is regarded as a task. To effectively exploit the complementary yet correlated information among different tasks, a flexible representation algorithm termed multitask adaptive sparse representation (MASR) is proposed. The MASR algorithm not only restricts Gabor features of one test sample to be jointly represented by training atoms from the same class but also promotes the selected atoms for these features to be varied within each class, thus allowing better representation. In addition, to use the local information, we operate the MASR on local regions of Gabor features. Then, by considering the structural characteristics of the face and the effects of the external interferences, a structural-residual weighting strategy is proposed to adaptively fuse the decision of each region. Experiments on various datasets verify the effectiveness of the proposed method in dealing with face occlusion, corruption, small number of training samples, as well as variations of lighting and expression.

A Survey: Face Recognition Techniques

Abstract: In this study, the existing techniques of face recognition are to be encountered along with their pros and cons to conduct a brief survey. The most general methods include Eigenface (Eigenfeatures), Hidden Markov Model (HMM), geometric based and template matching approaches. This survey actually performs analysis on these approaches in order to constitute face representations which will be discussed as under. In the second phase of the survey, factors affecting the recognition rates and processes are also discussed along with the solutions provided by different authors.

An experimental evaluation of different face recognition algorithms using closed circuit Television images

Abstract: Bio-metric based recognition is rapidly replacing the non-biometric based recognition systems. Face recognition is one of the most important bio-metric based recognition technique. Different algorithms exist for face recognition that works well on high resolution digital images. The aim of this work is to check whether these existing face recognition algorithms work on low quality Closed Circuit Television images? We have considered three such algorithms. These algorithms are Artificial Neural Network, Principal Component Analysis and Single Value Decomposition. We have experimentally evaluated these for Closed Circuit Television images. Our experiments show that using the CCTV images the accuracy rate of ANN for face recognition is 85%, for PCA the accuracy rate is 75% and for SVD the accuracy rate is 65%. The experiments also show that the accuracy of all the three algorithms reduces when the number of input faces increases.

Face Recognition and Gender Classification Using Orthogonal Nearest Neighbour Feature Line Embedding

Abstract: In this paper, a novel manifold learning algorithm for face recognition and gender classification - orthogonal nearest neighbour feature line embedding (ONNFLE) - is proposed. Three of the drawbacks of the nearest feature space embedding (NFSE) method are solved: the extrapolation/interpolation error, high computational load and non-orthogonal eigenvector problems. The extrapolation error occurs if the distance from a specified point to one line is small when that line passes through two farther points. The scatter matrix generated by the invalid discriminant vectors does not efficiently preserve the locally topological structure - incorrect selection reduces recognition. To remedy this, the nearest neighbour (NN) selection strategy was used in the proposed method. In addition, the high computational load was reduced using a selection strategy. The last problem involved solving the non- orthogonal eigenvectors found with the NFSE algorithm. The proposed algorithm generated orthogonal bases possessing more discriminating power. Experiments were conducted to demonstrate the effectiveness of the proposed algorithm.