Abdul Adeel Mohammed

Human face recognition based on multidimensional PCA and extreme learning machine

In this work, a new human face recognition algorithm based on bidirectional two dimensional principal component analysis (B2DPCA) and extreme learning machine (ELM) is introduced. The proposed method is based on curvelet image decomposition of human faces and a subband that exhibits a maximum standard deviation is dimensionally reduced using an improved dimensionality reduction technique. Discriminative feature sets are generated using B2DPCA to ascertain classification accuracy. Other notable contributions of the proposed work include significant improvements in classification rate, up to hundred folds reduction in training time and minimal dependence on the number of prototypes. Extensive experiments are performed using challenging databases and results are compared against state of the art techniques.

Shape from focus using fast discrete curvelet transform

A new method for focus measure computation is proposed to reconstruct 3D shape using image sequence acquired under varying focus plane. Adaptive histogram equalization is applied to enhance varying contrast across different image regions for better detection of sharp intensity variations. Fast discrete curvelet transform (FDCT) is employed for enhanced representation of singularities along curves in an input image followed by noise removal using bivariate shrinkage scheme based on locally estimated variance. The FDCT coefficients with high activity are exploited to detect high frequency variations of pixel intensities in a sequence of images. Finally, focus measure is computed utilizing neighborhood support of these coefficients to reconstruct the shape and a well-focused image of the scene being probed.

A fast recognition framework based on extreme learning machine using hybrid object information

This paper presents a new supervised learning scheme, which uses hybrid information i.e. global and local object information, for accurate identification and classification at considerably high speed both in training and testing phase. The first contribution of this paper is a unique image representation using bidirectional two-dimensional PCA and Ferns style approach to represent global and local information, respectively, of an object. Secondly, the application of extreme learning machine supports reliable recognition with minimum error and learning speed approximately thousands of times faster than traditional neural networks. The proposed method is capable of classifying various datasets in a fraction of second compared to other modern algorithms that require at least 2-3s per image [14].

An Efficient Algorithm for Focus Measure Computation in Constant Time

This letter presents an efficient algorithm for focus measure computation, in constant time, to estimate depth map using image sequences acquired at varying focus. Two major factors that complicate focus measure computation include neighborhood support and gradient detection for oriented intensity variations. We present a distinct focus measure based on steerable filters that is invariant to neighborhood size and accomplishes fast depth map estimation at a considerably faster speed compared to other well-documented methods. Steerable filters represent architecture to synthesize filters of arbitrary orientation using a linear combination of basis filters. Such synthesis is helpful to analytically determine the filter output as a function of orientation. Steerable filters remove inherent limitations of traditional gradient detection techniques which perform inadequately for oriented intensity variations and low textured regions.

An efficient fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization

Modern fingerprint image compression and reconstruction standards used by the US Federal Bureau of Investigation (FBI) are based upon the popular biorthogonal 9-7 discrete wavelet transform. Multiresolution analysis tools have been successfully applied to fingerprint image compression for more than a decade; we propose a novel fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization. Wave atoms decomposition has been specifically designed for enhanced representation of oscillatory patterns and to convey precise temporal and spatial information. Our proposed compression scheme is based upon multistage vector quantization of processed wave atoms representation of fingerprint images. Wave atoms expansion is processed using mathematical morphological operators to emphasize and retain significant coefficients for transmission. Quantized information is encoded using arithmetic entropy scheme. The proposed image compression standard outperforms other well established methods and achieves PSNR gain up to 8.07 dB in comparison to FBIs wavelet scalar quantization. Data mining, law enforcement, border security, and forensic applications can potentially benefit from our compression scheme.

Application of wave atoms decomposition and extreme learning machine for fingerprint classification

Law enforcement, border security and forensic applications are some of the areas where fingerprint classification plays an important role. A new technique based on wave atoms decomposition and bidirectional two-dimensional principal component analysis (B2DPCA) using extreme learning machine (ELM) for fast and accurate fingerprint image classification is proposed. The foremost contribution of this paper is application of two dimensional wave atoms decomposition on original fingerprint images to obtain sparse and efficient coefficients. Secondly, distinctive feature sets are extracted through dimensionality reduction using B2DPCA. ELM eliminates limitations of classical training paradigm; trains data at a considerably faster speed due to its simplified structure and efficient processing. Our algorithm combines optimization of B2DPCA and the speed of ELM to obtain a superior and efficient algorithm for fingerprint classification. Experimental results on twelve distinct fingerprint datasets validate the superiority of our proposed method.

A Novel Technique for Human Face Recognition Using Nonlinear Curvelet Feature Subspace

This paper proposes a novel human face recognition system using curvelet transform and Kernel based principal component analysis. Traditionally multiresolution analysis tools namely wavelets and curvelets have been used in the past for extracting and analyzing still images for recognition and classification tasks. Curvelet transform has gained significant popularity over wavelet based techniques due to its improved directional and edge representation capability. In the past features extracted from curvelet subbands were dimensionally reduced using principal component analysis for obtaining an enhanced representative feature set. In this work we propose to use an improved scheme using kernel based principal component analysis (KPCA) for a comprehensive feature set generation. KPCA performs a nonlinear principal component analysis (PCA) using an integral kernel operator function and obtains features that are more meaningful than the ones extracted using a linear PCA. Extensive experiments were performed on a comprehensive database of face images and superior performance of KPCA based human face recognition in comparison with state-of-the-art recognition is established.

A generic fingerprint image compression technique based on wave atoms decomposition

Modern fingerprint image compression and reconstruction standards used by the US Federal Bureau of Investigation (FBI) are based upon the popular 9/7 discrete wavelet transform. Multiresolution analysis tools have been successfully applied for fingerprint image compression for more than a decade; we propose a novel fingerprint image compression technique based on recently proposed wave atoms decomposition. Wave atoms decomposition has specifically been designed for enhanced representation of oscillatory patterns to convey temporal and spatial information. Our proposed compression scheme is based upon linear vector quantization of decomposed wave atoms representation of fingerprint images. Later quantized information is encoded with arithmetic entropy scheme. The proposed image compression standard outperforms the FBI fingerprint image compression standard, the wavelet scalar quantization (WSQ). Data mining, law enforcement, border security, and forensic applications can potentially benefit from our proposed compression scheme.

A Generic Moment Invariants Based Supervised Learning Framework for Classification Using Partial Object Information

We present a novel classification scheme which uses partial object information that is selected adaptively using modified distance transform and represented as moment invariants (Hu moments) to compensate for scale, translation and rotational transformation(s). The moment invariants of different parts of an object are learned using AdaBoost algorithm [1]. The classifier obtained using the proposed scheme is able to handle changes in illumination, pose, and varying inter-class and intra-class attributes. Partial information based classification shows robustness against object articulations, clutters, and occlusions. The first contribution of our proposed method is an adaptive selection of partial object information using modified distance transform that attempts to extract contours along with its neighborhood information in the form of blocks. Secondly, our proposed method is invariant to scaling, translation and rotation, and reliably classifies occluded objects using fractional information. Our proposed method achieved better detection and classification rate compared to other state-of-the-art schemes.

A robust object detection approach using boosted anisotropic multiresolution analysis

In an unconstrained environment, adaptive classifiers produce improved recognition. Fast discrete curvelet transform has recently gained attention due to its ability to capture singularities along curves far away from smooth regions. Therefore, curvelet coefficients contain enhanced representation of image details at different scales and orientations. We propose a new approach for object class detection based on curvelet feature subspace obtained using Kernel PCA (KPCA) and learned using AdaBoost scheme [1]. The first contribution of current paper is a unique representation of an image called curvelet feature subspace that preserves global structure, and supports reliable detection of singularities along curves which play a considerably important role in recognition. Second contribution of our proposed method is an adaptive selection of features obtained using anisotropic style multiresolution analysis for robust object detection of varied inter-class, and intra-class attributes. Our proposed method achieved better detection rate compared to state-of-the-art schemes.