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DCT Based Face Recognition  [cached]
Prof. Anand Najan,Prof. Mrs. A. C. Phadke
International Journal of Engineering Innovations and Research , 2012,
Abstract: A typical automatic face recognition system is composed of three parts: face detection, face alignment and face recognition. Conventionally, these three parts are processed in a bottom-up manner: face detection is performed first, then the results are passed to face alignment, and finally to face recognition. In this paper we will see the face recognition using DCT. The face recognition algorithm is based on appearances of Local facial regions that are represented with discrete cosine transform coefficients. This system exploits the feature extraction capabilities of the discrete cosine transform (DCT) and invokes certain normalization techniques that increase its robustness to variations in facial geometry and illumination. The method is tested on two databases first the standard database and second database of real images. High percent of recognition is achieved by varying the threshold.
Computerized Face Detection and Recognition  [PDF]
Vytautas Perlibakas
Computer Science , 2004,
Abstract: This publication presents methods for face detection, analysis and recognition: fast normalized cross-correlation (fast correlation coefficient) between multiple templates based face pre-detection method, method for detection of exact face contour based on snakes and Generalized Gradient Vector Flow field, method for combining recognition algorithms based on Cumulative Match Characteristics in order to increase recognition speed and accuracy, and face recognition method based on Principal Component Analysis of the Wavelet Packet Decomposition allowing to use PCA - based recognition method with large number of training images. For all the methods are presented experimental results and comparisons of speed and accuracy with large face databases.
Sparsity and Robustness in Face Recognition  [PDF]
John Wright,Arvind Ganesh,Allen Yang,Zihan Zhou,Yi Ma
Computer Science , 2011,
Abstract: This report concerns the use of techniques for sparse signal representation and sparse error correction for automatic face recognition. Much of the recent interest in these techniques comes from the paper "Robust Face Recognition via Sparse Representation" by Wright et al. (2009), which showed how, under certain technical conditions, one could cast the face recognition problem as one of seeking a sparse representation of a given input face image in terms of a "dictionary" of training images and images of individual pixels. In this report, we have attempted to clarify some frequently encountered questions about this work and particularly, on the validity of using sparse representation techniques for face recognition.
Face Recognition Methods & Applications  [PDF]
Divyarajsinh N. Parmar,Brijesh B. Mehta
Computer Science , 2014,
Abstract: Face recognition presents a challenging problem in the field of image analysis and computer vision. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, Offices, University, ATM, Bank and in any locations with a security system. Face recognition is a biometric system used to identify or verify a person from a digital image. Face Recognition system is used in security. Face recognition system should be able to automatically detect a face in an image. This involves extracts its features and then recognize it, regardless of lighting, expression, illumination, ageing, transformations (translate, rotate and scale image) and pose, which is a difficult task. This paper contains three sections. The first section describes the common methods like holistic matching method, feature extraction method and hybrid methods. The second section describes applications with examples and finally third section describes the future research directions of face recognition.
A unified framework for thermal face recognition  [PDF]
Reza Shoja Ghiass,Ognjen Arandjelovic,Hakim Bendada,Xavier Maldague
Computer Science , 2014,
Abstract: The reduction of the cost of infrared (IR) cameras in recent years has made IR imaging a highly viable modality for face recognition in practice. A particularly attractive advantage of IR-based over conventional, visible spectrum-based face recognition stems from its invariance to visible illumination. In this paper we argue that the main limitation of previous work on face recognition using IR lies in its ad hoc approach to treating different nuisance factors which affect appearance, prohibiting a unified approach that is capable of handling concurrent changes in multiple (or indeed all) major extrinsic sources of variability, which is needed in practice. We describe the first approach that attempts to achieve this - the framework we propose achieves outstanding recognition performance in the presence of variable (i) pose, (ii) facial expression, (iii) physiological state, (iv) partial occlusion due to eye-wear, and (v) quasi-occlusion due to facial hair growth.
An Introduction to Face Recognition Technology  [PDF]
Shang-Hung Lin
Informing Science The International Journal of an Emerging Transdiscipline , 2000,
Abstract: Recently face recognition is attracting much attention in the society of network multimedia information access. Areas such as network security, content indexing and retrieval, and video compression benefits from face recognition technology because "people" are the center of attention in a lot of video. Network access control via face recognition not only makes hackers virtually impossible to steal one's "password", but also increases the user-friendliness in human-computer interaction. Indexing and/or retrieving video data based on the appearances of particular persons will be useful for users such as news reporters, political scientists, and moviegoers. For the applications of videophone and teleconferencing, the assistance of face recognition also provides a more efficient coding scheme. In this paper, we give an introductory course of this new information processing technology. The paper shows the readers the generic framework for the face recognition system, and the variants that are frequently encountered by the face recognizer. Several famous face recognition algorithms, such as eigenfaces and neural networks, will also be explained.
The Issue of Face Recognition
C. Stavrianos,C. Papadopoulos,O. Pantelidou,J. Emmanouil,N. Petalotis
Research Journal of Medical Sciences , 2012, DOI: 10.3923/rjmsci.2012.163.165
Abstract: Recognizing the identities of people is a basic requirement for the establishment and maintenance of social act and communication and face recognition is an ability that humans develop and become very skilled as they grow up. Recognition has always been a very intriguing and highly researched topic and implies the tasks of identification or authentication. It is apparent that face recognition for human beings involves more than simple tasks of shape matching of features and face. Despite the fact that is not fully understood how humans recognise people what is known today is that they use a combination of identifiers such as height, voice and facial features.
Selection of Eigenvectors for Face Recognition  [PDF]
Manisha Satone,G.K.Kharate
International Journal of Advanced Computer Sciences and Applications , 2013,
Abstract: Face recognition has advantages over other biometric methods. Principal Component Analysis (PCA) has been widely used for the face recognition algorithm. PCA has limitations such as poor discriminatory power and large computational load. Due to these limitations of the existing PCA based approach, we used a method of applying PCA on wavelet subband of the face image and two methods are proposed to select best of the eigenvectors for recognition. The proposed methods select important eigenvectors using genetic algorithm and entropy of eigenvectors. Results show that compared to traditional method of selecting top eigenvectors, proposed method gives better results with less number of eigenvectors.
Face Recognition Using PCA  [cached]
Prakash S Mohod,Kalpna C Jondhale
International Journal of Artificial Intelligence & Knowledge Discovery , 2011,
Abstract: Our approach rates the face recognition problem as an intrinsically two-dimensional (2D) recognition problem ratherthan requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2D characteristic views. The system functions by Projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as “eigenfaces”, because they are the eigenvectors (Principle components) of the set of faces. They do not necessarily correspond to the features such as eyes, ears and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features and so to recognize a Particular face it is necessary only to compare these weights to those of known individuals.
Face frontalization for Alignment and Recognition  [PDF]
Christos Sagonas,Yannis Panagakis,Stefanos Zafeiriou,Maja Pantic
Computer Science , 2015,
Abstract: Recently, it was shown that excellent results can be achieved in both face landmark localization and pose-invariant face recognition. These breakthroughs are attributed to the efforts of the community to manually annotate facial images in many different poses and to collect 3D faces data. In this paper, we propose a novel method for joint face landmark localization and frontal face reconstruction (pose correction) using a small set of frontal images only. By observing that the frontal facial image is the one with the minimum rank from all different poses we formulate an appropriate model which is able to jointly recover the facial landmarks as well as the frontalized version of the face. To this end, a suitable optimization problem, involving the minimization of the nuclear norm and the matrix $\ell_1$ norm, is solved. The proposed method is assessed in frontal face reconstruction (pose correction), face landmark localization, and pose-invariant face recognition and verification by conducting experiments on $6$ facial images databases. The experimental results demonstrate the effectiveness of the proposed method.
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