175 research outputs found

    Palmprint Gender Classification Using Deep Learning Methods

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    Gender identification is an important technique that can improve the performance of authentication systems by reducing searching space and speeding up the matching process. Several biometric traits have been used to ascertain human gender. Among them, the human palmprint possesses several discriminating features such as principal-lines, wrinkles, ridges, and minutiae features and that offer cues for gender identification. The goal of this work is to develop novel deep-learning techniques to determine gender from palmprint images. PolyU and CASIA palmprint databases with 90,000 and 5502 images respectively were used for training and testing purposes in this research. After ROI extraction and data augmentation were performed, various convolutional and deep learning-based classification approaches were empirically designed, optimized, and tested. Results of gender classification as high as 94.87% were achieved on the PolyU palmprint database and 90.70% accuracy on the CASIA palmprint database. Optimal performance was achieved by combining two different pre-trained and fine-tuned deep CNNs (VGGNet and DenseNet) through score level average fusion. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was also implemented to ascertain which specific regions of the palmprint are most discriminative for gender classification

    Hand Geometry Techniques: A Review

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    Volume 2 Issue 11 (November 2014

    Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review

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    This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen

    LEARNING-FREE DEEP FEATURES FOR MULTISPECTRAL PALM-PRINT CLASSIFICATION

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    The feature extraction step is a major and crucial step in analyzing and understanding raw data as it has a considerable impact on the system accuracy. Unfortunately, despite the very acceptable results obtained by many handcrafted methods, they can have difficulty representing the features in the case of large databases or with strongly correlated samples. In this context, we proposed a new, simple and lightweight method for deep feature extraction. Our method can be configured to produce four different deep features, each controlled to tune the system accuracy. We have evaluated the performance of our method using a multispectral palmprint based biometric system and the experimental results, using the CASIA database, have shown that our method has high accuracy compared to many current handcrafted feature extraction methods and many well known deep learning based methods

    Fingerprint-Matching Algorithm Using Polar Shapelets

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    An image, such as a fingerprint, can be decomposed into a linear combination of polar shapelet-base functions. This publication describes a fingerprint-matching algorithm that uses polar shapelet-base functions. Using polar shapelet-base functions, a fingerprint image block can be separated into components with explicit rotational symmetries. Polar shapelet-base functions can represent the fingerprint image through compact parameterization or encoder representation due to their interpretation in terms of the rotational angle , and due to their separability by a distance r. Therefore, the use of polar shapelets enables a convenient and robust method to perform fingerprint image manipulation, analysis, and matching. Polar shapelet-base functions are special types of steerable filters that have rotational symmetry. When the fingerprint image is convolved with a polar shapelet-base function, the magnitude of the convolution output is rotationally invariant, and the relative rotation between two fingerprint images is the phase shift in the convolution output, which enables calculating the rotation angle between two matching image blocks with relative ease. Polar shapelet-base functions can be utilized to create a machine-learned (ML) model that is composed of harmonic and rotationally symmetric convolution filters. The fingerprint-matching algorithm pre-specifies the rotation order of each filter, but the size and shape of the convolution filter is optimized using the ML model. Also, the fingerprint-matching algorithm optimizes a TensorFlow implementation for each convolution filter in the radial direction r. The ML model determines an optimized set of filters that can increase the matching between two rotated images. The described fingerprint-matching algorithm offers high-resolution fingerprint images, low computation latency, low image energy residuals, and high matching rates

    Hand-based multimodal identification system with secure biometric template storage

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    WOS:000304107200001This study proposes a biometric system for personal identification based on three biometric characteristics from the hand, namely: the palmprint, finger surfaces and hand geometry. A protection scheme is applied to the biometric template data to guarantee its revocability, security and diversity among different biometric systems. An error-correcting code (ECC), a cryptographic hash function (CHF) and a binarisation module are the core of the template protection scheme. Since the ECC and CHF operate on binary data, an additional feature binarisation step is required. This study proposes: (i) a novel identification architecture that uses hand geometry as a soft biometric to accelerate the identification process and ensure the system's scalability; and (ii) a new feature binarisation technique that guarantees that the Hamming distance between transformed binary features is proportional to the difference between their real values. The proposed system achieves promising recognition and speed performances on two publicly available hand image databases.info:eu-repo/semantics/acceptedVersio
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