Mohamed El Aroussi

Local appearance based face recognition method using block based steerable pyramid transform

In this paper, an efficient local appearance feature extraction method based on Steerable Pyramid (S-P) wavelet transform is proposed for face recognition. Local information is extracted by computing the statistics of each sub-block obtained by dividing S-P sub-bands. The obtained local features of each sub-band are combined at the feature and decision level to enhance face recognition performance. The purpose of this paper is to explore the usefulness of S-P as feature extraction method for face recognition. The proposed approach is compared with some related feature extraction methods such as principal component analysis (PCA), as well as linear discriminant analysis LDA and boosted LDA. Different multi-resolution transforms, wavelet (DWT), gabor, curvelet and contourlet, are also compared against the block-based S-P method. Experimental results on ORL, Yale, Essex and FERET face databases convince us that the proposed method provides a better representation of the class information, and obtains much higher recognition accuracies in real-world situations including changes in pose, expression and illumination.

A Uniform Balancing Energy Routing Protocol for Wireless Sensor Networks

In wireless sensor network, the power supply is, generally, a non-renewable battery. Consequently, energy effectiveness is a crucial factor. To maximize the battery life and therefore, the duration of network service, a robust wireless communication protocol providing a best energy efficiency is required. In this paper, we present a uniform balancing energy routing protocol. In this later the transmission path is chosen for maximizing the whole network lifetime. Every transmission round, only the nodes which have their remaining energies greater than a threshold can participate as routers for other nodes in addition to sensing the environment. This choice allows the distribution of energy load among any sensor nodes; thus extends network lifetime. The experimental results shows that the proposed protocol outperforms some protocols given in the literature.

Curvelet-based feature extraction with B-LDA for face recognition

In this paper, we propose a novel feature extraction scheme based on the multi-resolution curvelet transform for face recognition. The obtained curvelet coefficients act as the feature set for classification, and are used to train the ensemble-based discriminant learning approach, capable of taking advantage of both the boosting and LDA (BLDA) techniques. The proposed method CV-BLDA has been extensively assessed using different databases: the ATT, YALE and FERET, Tests indicate that using curvelet-based features significantly improves the accuracy compared to standard face recognition algorithms and other multi-resolution based approaches.

An energy efficient clustering protocol for routing in Wireless Sensor Network

In Wireless Sensor Network (WSN), the power supply is generally a non-renewable battery, consequently, energy effectiveness is a crucial factor. To maximise the battery life and therefore the duration of network service, a robust wireless communication protocol providing a best energy efficiency is required. In this paper, we present an Enhanced Low Energy Clustering Protocol for Routing in WSN (ELECP). The ELECP is a decentralised clustering algorithm that can be used in the case where the area sensed data are not perfectly correlated. The technique, used to partition the network, allows the distribution of energy load among any sensor nodes; this extends network lifetime. Simulation results show that the network lifetime is increased largely comparable to existing schemes.

Block based curvelet feature extraction for face recognition

In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed for face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such as Principal component analysis (PCA), as well as Linear Discriminant Analysis LDA and Boosted LDA (BLDA). Two different muti-resolution transforms, Wavelet (DWT) and Contourlet, were also compared against the Block Based Curvelet algorithm. Experimental results on ORL, Yale and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.

Combining DCT and LBP Feature Sets For Efficient Face Recognition

In this paper, we present a novel approach for face recognition combining classifiers based on both micro texture in spatial domain provided by local binary pattern (LBP) and macro information in frequency domain acquired from the discrete cosine transform (DCT) to represent facial image. The classification of these two feature sets is performed by using support vector machines (SVMs), which had been shown to be superior to traditional pattern classifiers. The experiments clearly show the superiority of the proposed classifier combination approaches over individual classifiers on the Yale face database and a high correct classification rate of 96% is obtained.

Novel face recognition approach based on steerable pyramid feature extraction

In this paper, an efficient local appearance feature extraction method based steerable pyramid (S-P) is proposed for face recognition. Local information is extracted from S-P sub-bands using block-based statistics. The underlying statistics allow us to reduce the required amount of data to be stored. The obtained local features are combined at the feature and decision level to enhance face recognition performance. Experimental results on ORL, Yale and FERET face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.

Finger knuckle print recognition based on multi-instance fusion of local feature sets

Biometrics has become one of the reliable averages to construct the recognition systems of personal identity. Recent studies have attracted the attention of researchers for a new method finger-knuckle-print (FKP), which focuses on the related skin patterns of the outer surface around the phalangeal joint of ones finger. It was discovered that the finger-knuckle print (FKP) allows discrimination between different people. Adaptation of feature extraction and matching to increase the distinction effectively between individuals plays a key role in such an FKP based personal authentication system. In this paper, we present a novel approach use of multi-instance feature fusion based on micro texture in spatial domain provided by uniform local binary pattern (ULBP) to circumvent the influence problem of the sub-image size on the recognition rate. For classification, we have used the minimum distance classifier and experimented with two different distance measures: Euclidean and City-block. The experiments clearly show the superiority of the multi-instance verification approach than using any single instance verification over individual classifiers on the published PolyU knuckle database.

Fusion of face and iris features extraction based on steerable pyramid representation for multimodal biometrics

In this paper, we make a first attempt to combine face and iris biometrics using an efficient local appearance feature extraction method based on steerable pyramid (S-P), to captures the intrinsic geometrical structures of face and iris image, it decomposes the face and iris image into a set of directional sub-bands with texture details captured in different orientations at various scales. Local information is extracted from S-P sub-bands using block-based statistics to reduce the required amount of data to be stored. The obtained local features are combined at the score level for developing a multimode biometric approach, which is able to diminish the drawback of single biometric approach as well as to improve the performance of authentication system. We combine a face database FERET and iris database CASIA (version 1) to construct a multimodal biometric experimental database with which we validate the proposed approach and evaluate the multimodal biometrics performance. The experimental results reveal the multimodal biometric authentication is much more reliable and precise than single biometric approach.

A New Robust Blind Copyright Protection Scheme Based on Visual Cryptography and Steerable Pyramid

In this paper, we proposed a novel blind digital image copyright protection scheme based on Steerable pyramid transform (SPT) and visual cryptography (VC). Unlike traditional watermarking schemes, the proposed method does not alter the original image by embedding the watermark image. Steerable pyramid transform is performed on the original image, and the low sub-band is selected. The watermark image is divided into two random looking images, called private and public shares using the visual secret sharing scheme and the selected low sub-band features. To reveal the watermark image, the two shares are stacked together while using each share separately reveals no information about the watermark image. A series of attacking experiments are performed on the original image to test the robustness of the proposed method. The experimental results show excellent visual imperceptibility and robustness against a variety of attacks.