Palm print biometric recognition based on Scattering Wavelet Transform

2016 ◽  
Author(s):  
Saranraj S ◽  
Padmapriya V ◽  
Sudharsan S ◽  
Piruthiha D ◽  
Venkateswaran N
Keyword(s):  
Wavelet Transform ◽  
Biometric Recognition ◽  
Palm Print

Iris-based biometric recognition using dyadic wavelet transform

2002 ◽  
Vol 17 (10) ◽  
pp. 3-6 ◽  
Author(s):  
C. Sanchez-Avila ◽  
R. Sanchez-Reillo ◽  
D. de Martin-Roche
Keyword(s):  
Wavelet Transform ◽  
Biometric Recognition ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform

scholarly journals Human gait-based gender classification using neutral and non-neutral gait sequences

2019 ◽  
Vol 7 (1) ◽  
pp. 1-13
Author(s):  
Zhyar Q. Mawlood ◽  
Azhin T. Sabir
Keyword(s):  
Wavelet Transform ◽  
Experimental Result ◽  
Human Gait ◽  
Biometric Identification ◽  
Automatic Identification ◽  
Biometric System ◽  
Biometric Recognition ◽  
Gait Features ◽  
The Individual ◽  
Gait Database

A biometric system offers automatic identification of an individual basedon characteristic possessed by the individual. Biometric identification systems are often categorized as physiological or behavioural characteristics.Gait as one of the behavioural biometric recognition aims to recognizean individual by the way he/she walk. In this paper we propose genderclassification based on human gait features using wavelet transform andinvestigates the problem of non-neutral gait sequences; Coat Wearing andcarrying bag condition as addition to the neutral gait sequences. We shallinvestigate a new set of feature that generated based on the Gait Energy Image and Gait Entropy Image called Gait Entropy Energy Image(GEnEI). Three different feature sets constructed from GEnEI basedon wavelet transform called, Approximation coefficient Gait EntropyEnergy Image, Vertical coefficient Gait Entropy Energy Image and Approximation & Vertical coefficients Gait Entropy Energy Image Finallytwo different classification methods are used to test the performance ofthe proposed method separately, called k-nearest-neighbour and SupportVector Machine. Our tests are based on a large number of experimentsusing a well-known gait database called CASIA B gait database, includes124 subjects (93 males and 31 females). The experimental result indicatesthat the proposed method provides significant results and outperform thestate of the art.

scholarly journals Synthesis of Palm Print in Feature Fusion Techniques for Multimodal Biometric Recognition System Online Signature

2021 ◽  
Vol 3 (2) ◽  
pp. 131-143
Author(s):  
Vijayakumar T.
Keyword(s):  
Feature Fusion ◽  
Developed Countries ◽  
Biometric Recognition ◽  
Accurate Identification ◽  
Multiple Features ◽  
Research Attention ◽  
Research Article ◽  
Biometric Systems ◽  
Palm Print ◽  
Multimodal Biometric Recognition

Biometric identification technology is widely utilized in our everyday lives as a result of the rising need for information security and safety laws throughout the world. In this aspect, multimodal biometric recognition (MBR) has gained significant research attention due to its ability to overcome several important constraints in unimodal biometric systems. Henceforth, this research article utilizes multiple features such as an iris, face, finger vein, and palm print for obtaining the highest accuracy to identify the exact person. The utilization of multiple features from the person improves the accuracy of biometric system. In many developed countries, palm print features are employed to provide the most accurate identification of an actual individual as fast as possible. The proposed system can be very suitable for the person who dislikes answering many questions for security authentication. Moreover, the proposed system can also be used to minimize the extra questionnaire by achieving a highest accuracy than other existing multimodal biometric systems. Finally, the results are computed and tabulated in this research article.

scholarly journals A Robust Texture Based Ear and Palmprint Recognition Using Histogram of Oriented Gradients

2019 ◽  
Vol 8 (6) ◽  
pp. 1964-1971
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Image Features ◽  
Palmprint Recognition ◽  
Histogram Of Oriented Gradients ◽  
Biometric Recognition ◽  
Local Descriptor ◽  
Depth Data ◽  
Palm Print ◽  
Level Fusion

Recent research in the surface-based ear and palm print recognition additionally shows that ear identification and palm print identification. The surface-based ear and palm print recognition are strong against sign corruption and encoding antiques. Based on these discoveries, further research and look at the comparison of surface descriptors for ear and palm print recognition and try to investigate potential outcomes to supplement surface descriptors with depth data. The proposed Multimodal ear and palm print Biometric Recognition work is based on the feature level fusion. Based on the ear images and palm print images from noticeable brightness as well as profundity records, we remove surface with outside labels starting complete contour images. In this paper, think about the recognition performance of choose strategies for describing the surface structure, which is Local Binary Pattern (LBP), Weber Local Descriptor (WLD), Histogram of oriented gradients (HOG), and Binarised Statistical Image Features (BSIF). The broad test examination dependent scheduled target IIT Delhi-2 ear and IIT Delhi palm print records affirmed to facilitate and expected multimodal biometric framework can build recognition rates contrasted and that delivered by single-modular for example, Unimodal biometrics. The proposed method Histogram of Oriented Gradients (HOG) achieving a recognition rate of 124%

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