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Performance Comparison of Face Recognition Using DCT Against Face Recognition Using Vector Quantization Algorithms LBG, KPE, KMCG, KFCG
Shachi J. Natu,Prachi J. Natu,Tanuja K. Sarode,H. B. Kekre
International Journal of Image Processing , 2010,
Abstract: In this paper, a face recognition system using simple Vector quantization (VQ) technique is proposed. Four different VQ algorithms namely LBG, KPE, KMCG and KFCG are used to generate codebooks of desired size. Euclidean distance is used as similarity measure to compare the feature vector of test image with that of trainee images. Proposed algorithms are tested on two different databases. One is Georgia Tech Face Database which contains color JPEG images, all are of different size. Another database used for experimental purpose is Indian Face Database. It contains color bitmap images. Using above VQ techniques, codebooks of different size are generated and recognition rate is calculated for each codebook size. This recognition rate is compared with the one obtained by applying DCT on image and LBG-VQ algorithm which is used as benchmark in vector quantization. Results show that KFCG outperforms other three VQ techniques and gives better recognition rate up to 85.4% for Georgia Tech Face Database and 90.66% for Indian Face Database. As no Euclidean distance computations are involved in KMCG and KFCG, they require less time to generate the codebook as compared to LBG and KPE
Regularized Robust Coding for Face Recognition  [PDF]
Meng Yang,Lei Zhang,Jian Yang,David Zhang
Computer Science , 2012, DOI: 10.1109/TIP.2012.2235849
Abstract: Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1-norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes SRC's computational cost very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (IR3C) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting and expression changes, etc.
Robust Face Recognition Using Distance Matrice  [PDF]
Chirag I. Patel,Ripal Patel
International Journal of Computer and Electrical Engineering , 2013, DOI: 10.7763/ijcee.2013.v5.740
Abstract: In this paper, we have present the face recognition method based on partial Hausdorff distance. Normally face recognition algorithm gives poor results against pose and illumination variation. But the algorithm we have presented is robust to those conditions. We have applied transformation on face image which is robust todifferent face pose and illumination variations. Then the partial Hausdorff distance is calculated for matching after that the performance of face recognition is evaluated on different database.
A robust, low-cost approach to Face Detection and Face Recognition  [PDF]
Divya Jyoti,Aman Chadha,Pallavi Vaidya,M. Mani Roja
Computer Science , 2011,
Abstract: In the domain of Biometrics, recognition systems based on iris, fingerprint or palm print scans etc. are often considered more dependable due to extremely low variance in the properties of these entities with respect to time. However, over the last decade data processing capability of computers has increased manifold, which has made real-time video content analysis possible. This shows that the need of the hour is a robust and highly automated Face Detection and Recognition algorithm with credible accuracy rate. The proposed Face Detection and Recognition system using Discrete Wavelet Transform (DWT) accepts face frames as input from a database containing images from low cost devices such as VGA cameras, webcams or even CCTV's, where image quality is inferior. Face region is then detected using properties of L*a*b* color space and only Frontal Face is extracted such that all additional background is eliminated. Further, this extracted image is converted to grayscale and its dimensions are resized to 128 x 128 pixels. DWT is then applied to entire image to obtain the coefficients. Recognition is carried out by comparison of the DWT coefficients belonging to the test image with those of the registered reference image. On comparison, Euclidean distance classifier is deployed to validate the test image from the database. Accuracy for various levels of DWT Decomposition is obtained and hence, compared.
Structured Occlusion Coding for Robust Face Recognition  [PDF]
Yandong Wen,Weiyang Liu,Meng Yang,Yuli Fu,Youjun Xiang,Rui Hu
Computer Science , 2015,
Abstract: Occlusion in face recognition is a common yet challenging problem. While sparse representation based classification (SRC) has been shown promising performance in laboratory conditions (i.e. noiseless or random pixel corrupted), it performs much worse in practical scenarios. In this paper, we consider the practical face recognition problem, where the occlusions are predictable and available for sampling. We propose the structured occlusion coding (SOC) to address occlusion problems. The structured coding here lies in two folds. On one hand, we employ a structured dictionary for recognition. On the other hand, we propose to use the structured sparsity in this formulation. Specifically, SOC simultaneously separates the occlusion and classifies the image. In this way, the problem of recognizing an occluded image is turned into seeking a structured sparse solution on occlusion-appended dictionary. In order to construct a well-performing occlusion dictionary, we propose an occlusion mask estimating technique via locality constrained dictionary (LCD), showing striking improvement in occlusion sample. On a category-specific occlusion dictionary, we replace norm sparsity with the structured sparsity which is shown more robust, further enhancing the robustness of our approach. Moreover, SOC achieves significant improvement in handling large occlusion in real world. Extensive experiments are conducted on public data sets to validate the superiority of the proposed algorithm.
Discriminative Local Sparse Representations for Robust Face Recognition  [PDF]
Yi Chen,Umamahesh Srinivas,Thong T. Do,Vishal Monga,Trac D. Tran
Computer Science , 2011,
Abstract: A key recent advance in face recognition models a test face image as a sparse linear combination of a set of training face images. The resulting sparse representations have been shown to possess robustness against a variety of distortions like random pixel corruption, occlusion and disguise. This approach however makes the restrictive (in many scenarios) assumption that test faces must be perfectly aligned (or registered) to the training data prior to classification. In this paper, we propose a simple yet robust local block-based sparsity model, using adaptively-constructed dictionaries from local features in the training data, to overcome this misalignment problem. Our approach is inspired by human perception: we analyze a series of local discriminative features and combine them to arrive at the final classification decision. We propose a probabilistic graphical model framework to explicitly mine the conditional dependencies between these distinct sparse local features. In particular, we learn discriminative graphs on sparse representations obtained from distinct local slices of a face. Conditional correlations between these sparse features are first discovered (in the training phase), and subsequently exploited to bring about significant improvements in recognition rates. Experimental results obtained on benchmark face databases demonstrate the effectiveness of the proposed algorithms in the presence of multiple registration errors (such as translation, rotation, and scaling) as well as under variations of pose and illumination.
Robust multi-camera view face recognition  [PDF]
Dakshina Ranjan Kisku,Hunny Mehrotra,Phalguni Gupta,Jamuna Kanta Sing
Computer Science , 2010,
Abstract: This paper presents multi-appearance fusion of Principal Component Analysis (PCA) and generalization of Linear Discriminant Analysis (LDA) for multi-camera view offline face recognition (verification) system. The generalization of LDA has been extended to establish correlations between the face classes in the transformed representation and this is called canonical covariate. The proposed system uses Gabor filter banks for characterization of facial features by spatial frequency, spatial locality and orientation to make compensate to the variations of face instances occurred due to illumination, pose and facial expression changes. Convolution of Gabor filter bank to face images produces Gabor face representations with high dimensional feature vectors. PCA and canonical covariate are then applied on the Gabor face representations to reduce the high dimensional feature spaces into low dimensional Gabor eigenfaces and Gabor canonical faces. Reduced eigenface vector and canonical face vector are fused together using weighted mean fusion rule. Finally, support vector machines (SVM) have trained with augmented fused set of features and perform the recognition task. The system has been evaluated with UMIST face database consisting of multiview faces. The experimental results demonstrate the efficiency and robustness of the proposed system for multi-view face images with high recognition rates. Complexity analysis of the proposed system is also presented at the end of the experimental results.
Robust Face Recognition by Constrained Part-based Alignment  [PDF]
Yuting Zhang,Kui Jia,Yueming Wang,Gang Pan,Tsung-Han Chan,Yi Ma
Computer Science , 2015,
Abstract: Developing a reliable and practical face recognition system is a long-standing goal in computer vision research. Existing literature suggests that pixel-wise face alignment is the key to achieve high-accuracy face recognition. By assuming a human face as piece-wise planar surfaces, where each surface corresponds to a facial part, we develop in this paper a Constrained Part-based Alignment (CPA) algorithm for face recognition across pose and/or expression. Our proposed algorithm is based on a trainable CPA model, which learns appearance evidence of individual parts and a tree-structured shape configuration among different parts. Given a probe face, CPA simultaneously aligns all its parts by fitting them to the appearance evidence with consideration of the constraint from the tree-structured shape configuration. This objective is formulated as a norm minimization problem regularized by graph likelihoods. CPA can be easily integrated with many existing classifiers to perform part-based face recognition. Extensive experiments on benchmark face datasets show that CPA outperforms or is on par with existing methods for robust face recognition across pose, expression, and/or illumination changes.
Robust Face Recognition through Local Graph Matching  [cached]
Ehsan Fazl-Ersi,John S. Zelek,John Tsotsos
Journal of Multimedia , 2007, DOI: 10.4304/jmm.2.5.31-37
Abstract: A novel face recognition method is proposed, in which face images are represented by a set of local labeled graphs, each containing information about the appearance and geometry of a 3-tuple of face feature points, extracted using Local Feature Analysis (LFA) technique. Our method automatically learns a model set and builds a graph space for each individual. A two-stage method for optimal matching between the graphs extracted from a probe image and the trained model graphs is proposed. The recognition of each probe face image is performed by assigning it to the trained individual with the maximum number of references. Our approach achieves perfect result on the ORL face set and an accuracy rate of 98.4% on the FERET face set, which shows the superiority of our method over all considered state-of-the-art methods. I
Robust Face Recognition under Difficult Lighting Conditions  [PDF]
S.S. Ghatge,V.V. Dixit
International Journal of Technological Exploration and Learning , 2012,
Abstract: — This paper addresses the problem of illumination effects on Face recognition and works for an approach to reduce their effect on recognition performance. More broadly, a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition.Using local ternary patterns (LTP), a generalization of the local binary pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions. We also show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition; and Robustness is still improved by adding Kernel principal component analysis (PCA) feature extraction.
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