Hassan Masood

Wavelet based palmprint authentication system

Palmprint based personal verification has quickly entered the biometric family due to its ease of acquisition, high user acceptance and reliability. This paper proposes a palm print based identification system using the textural information, employing different wavelet transforms. The transforms employed have been analyzed for their individual as well as combined performances at feature level. The wavelets used for the analysis are Biorthogonal, Symlet and Discrete Meyer. The analysis of these wavelets is carried out on 500 images, acquired through indigenously made image acquisition system. 500 palmprint obtained from 50 users with 10 samples each have been collected over a period of six months and have been evaluated for the performance of the proposed system. The experimental results obtained from the data have demonstrated the feasibility of the proposed system by exhibiting Genuine Acceptance Rate, GAR of 97.12%.

A feature level multimodal approach for palmprint identification using directional subband energies

Palmprint based personal identification has gained preference over other biometric modalities due to its ease of acquisition, high user acceptance and reliability. This paper presents a palmprint based identification approach which uses the textural information available on the palmprint by employing a feature level fusion of contourlet transform (CT) and non-subsampled contourlet transform (NSCT). The proposed algorithm captures both local and global details in a palmprint as a compact fixed length palm code. After establishing the region of interest (ROI), the two-dimensional (2-D) spectrum is divided into fine slices using iterated directional filterbanks. Next, directional energy component for each block from the decomposed subband outputs is computed separately for the two transforms. The features from both domains are then fused at feature levels. Palmprint matching is then performed using normalized Euclidean distance classifier. The algorithm is tested on complete database of 7752 palm images of Polytechnic University of Hong Kong, and 500 palm images of GPDS Hand database from University of Las Palmas de Gran Canaria. The experimental results were compiled for features based upon individual transforms and fused one. CT based approach demonstrated the decidability index of 2.7734 and equal error rate (EER) of 0.2333% while NSCT based approach depicted decidability index of 2.8125 and EER of 0.1604% on palm database of Polytechnic University of Hong Kong. Similarly, CT based approach demonstrated the decidability index of 2.6212 and equal error rate (EER) of 0.7082% while NSCT based approach depicted decidability index of 2.7278 and EER of 0.5082% on GPDS hand database. The multimodal approach based upon feature fusion achieved decidability index of 2.8914 and EER of 0.1563% on database of Polytechnic University of Hong Kong and decidability index of 2.7956 and EER of 0.3112% on GPDS hand database. The quantitative measures confirm progressive improved results in three approaches for both the databases.

Combined Contourlet and Non-subsampled Contourlet Transforms Based Approach for Personal Identification Using Palmprint

Palmprint based personal verification is an accepted biometric modality due to its reliability, ease of acquisition and user acceptance. This paper presents a novel palmprint based identification approach which draw on the textural information available on the palmprint by utilizing a combination of Contourlet and Non Subsampled Contourlet Transforms. Center of the palm is computed using the Distance Transform whereas the parameters of best fitting ellipse help determine the alignment of the palmprint. ROI of 256X256 pixels is cropped around the center, and subsequently it is divided into fine slices, using iterated directional filterbanks. Next, directional energy components for each block of the decomposed subband outputs are computed using Contourlet and Non Subsampled Contourlet Transforms. The proposed algorithm captures global details in a palmprint as compact fixed length palm codes for Contourlet and NSCT respectively which are further concatenated at feature level for identification using normalized Euclidean distance classifier. The proposed algorithm is tested on a total of 500 palm images of GPDS Hand database, acquired from University of Las Palmas de Gran Canaria. The experimental results were compiled for individual transforms as well as for their optimized combination at feature level. CT based approach demonstrated the Decidability Index of 2.6212 and Equal Error Rate (EER) of 0.7082% while NSCT based approach depicted Decidability Index of 2.7278 and EER of 0.5082%. The feature level fusion achieved Decidability Index of 2.7956 and EER of 0.3112%.

Palmprint Identification Using Contourlet Transform

Palmprint based personal verification has gained preference over other biometric modalities due to its ease of acquisition, high user acceptance and reliability. This paper presents a novel palmprint based identification approach which uses the textural information available on the palmprint by employing the Contourlet Transform (CT). After establishing the region of interest (ROI), the two dimensional (2-D) spectrums is divided into fine slices, using iterated directional filterbanks. Next, directional energy component for each block from the decomposed subband outputs is computed. The proposed algorithm captures both local and global details in a palmprint as a compact fixed length palm code. Palmprint matching is then performed using normalized Euclidean distance classifier. The proposed algorithm is tested on a total of 7752 palm images, acquired from the standard database of Polytechnic University of Hong Kong. The experimental results demonstrated the feasibility of the proposed system by exhibiting genuine acceptance rate of 88.91%, decidability index of 2.7748 and equal ierror rate of 0.2333%.

Personal identification using feature and score level fusion of palm- and fingerprints

The ever increasing demand of security has resulted in wide use of Biometric systems. Despite overcoming the traditional verification problems, the unimodal systems suffer from various challenges like intra class variation, noise in the sensor data etc, affecting the system performance. These problems are effectively handled by multimodal systems. In this paper, we present multimodal approach for palm- and fingerprints by feature level and score level fusions (sum and product rules). The proposed multi-modal systems are tested on a developed database consisting of 440 palm- and fingerprints each of 55 individuals. In feature level fusion, directional energy-based feature vectors of palm- and fingerprint identifiers are combined to form joint feature vector that is subsequently used to identify the individual using a distance classifier. In score level fusion, the matching scores of individual classifiers are fused by sum and product rules. Receiver operating characteristics curves are formed for unimodal and multimodal systems. Equal Error Rate (EER) of 0.538% for feature level fusion shows best performance compared to score level fusion of 0.6141 and 0.5482% of sum and product rules, respectively. Multimodal systems, however, significantly outperform unimodal palm- and fingerprints identifiers with EER of 2.822 and 2.553%, respectively.

Directional energy based palmprint identification using Non Subsampled Contourlet Transform

Palmprint based personal verification has gained preference over other biometric modalities due to its ease of acquisition, high user acceptance and reliability. This paper presents a novel palmprint based identification approach which uses the textural information available on the palmprint by employing the Non Subsampled Contourlet Transform (NSCT). After establishing the region of interest (ROI), the two dimensional (2-D) spectrum is divided into fine slices, using iterated directional filterbanks. Next, directional energy component for each block from the decomposed subband outputs is computed. The proposed algorithm captures both local and global details in a palmprint as a compact fixed length palm code. Palmprint matching is then performed using Normalized Euclidean Distance classifier. The algorithm is tested on a total of 7752 palm images, acquired from the standard database of Polytechnic University of Hong Kong. The experimental results demonstrated the feasibility of the proposed system by exhibiting Decidability Index of 2.8125 and Equal Error Rate of 0.1604%, better than the reported techniques in literature.

A New Approach to Aircraft Surface Inspection Based on Directional Energies of Texture

Non Destructive Inspections (NDI) plays a vital role in aircraft industry as it determines the structural integrity of aircraft surface and material characterization. The existing NDI methods are time consuming, we propose a new NDI approach using Digital Image Processing that has the potential to substantially decrease the inspection time. The aircraft imagery is analyzed by two methods i.e Contourlet Transform (CT) and Discrete Cosine Transform (DCT). With the help of Contourlet Transform the two dimensional (2-D) spectrum is divided into fine slices, using iterated directional filter banks. Next, directional energy components for each block of the decomposed subband outputs are computed. These energy values are used to distinguish between the crack and scratch images using the Dot Product classifier. In next approach, the aircraft imagery is decomposed into high and low frequency components using DCT and the first order moment is determined to form feature vectors. A correlation based approach is then used for distinction between crack and scratch surfaces. A comparative examination between the two techniques on a database of crack and scratch images revealed that texture analysis using the combined transform based approach gave the best results by giving an accuracy of 96.6% for the identification of crack surfaces and 98.3% for scratch surfaces.

Directional Energy Based Feature Level Multimodal System Using Palm and Fingerprints

The ever increasing demand of security has resulted in wide use of Biometric systems. Despite overcoming the traditional verification problems, the unimodal systems suffer from various challenges like intra class variation, noise in the sensor data etc, affecting the system performance. These problems are effectively handled by multimodal systems. In this paper, we present a feature level fused multimodal approach using palm and finger prints. Directional energy based feature vectors of palm and fingerprint identifiers are combined to form joint feature vector that is subsequently used to identify the individual using a distance classifier. The proposed multimodal system is tested on a developed database consisting of 440 palm and finger prints each of 55 individuals. Receiver Operating Characteristics curves are formed for unimodal and multimodal systems. Equal Error Rate (EER) of 0.538% for multimodal system depicts improved performance compared to 2.822% and 2.553% of palm and finger prints identifiers respectively.

An evaluation of wavelet kernels for palmprint based recognition

Palmprint based Identification is gaining popularity due to its traits like user acceptance, reliability and ease of acquisition. The paper presents a recognition method which extorts textural information obtainable from the palmprint, utilizing different filters of wavelet transform. Palmprint center has been computed using the chessboard metric of Distance Transform whereas the structures of best fitting ellipse help resolve the alignment of the palmprint. Region Of Interest of 256×256 pixels is clipped around the center. Next, normalized directional energy components of the decomposed subband outputs are computed for each block. Biorthogonal, Symlet, Discrete Meyer, Coiflet, Daubechies and Mexican hat wavelets are investigated on 500 palmprints acquired from 50 users with 10 samples each for their individual and concatenated combined features vectors. The performance has been analyzed using Euclidean classifier. An Equal Error Rate (EER) of 0.0217 and Genuine Acceptance Rate (GAR) of 97.12% with combined feature vector formed by Bior3.9, Sym8 and Dmeyer wavelets depict better performance over individual wavelet transforms and combination of coiflet, Daubechies and Mexican hat wavelets.