Abstract
Biometrics is the detection and description of individuals’ physiological and behavioral features. Many different systems require reliable personal identification schemes to either prove or find out the identity of an individual demanding their services. Multi-biometrics are required inside the current context of large worldwide biometric databases and to provide new developing security demands. There are some distinctive and measurable features used to distinguish individuals known as Biometric Identifiers. Multi-biometric systems tend to integrate multiple identifiers to increase recognition accuracy. Face and digital signature identifiers are still a challenge in many applications, especially in security systems. The fundamental objective of this paper is to integrate both identifiers in an accurate personal identification model. In this paper, a reliable multi-biometric model based on Histogram of Oriented Gradients (HOG) features of a face and digital signature and is able to identify individuals accurately is proposed. The methodology is to adopt many parameters such as weights of HOG features in merging process, the HOG parameters itself, and the distance method in matching process to gain higher accuracy. The proposed model achieves perfect results in personal identification using HOG features of digital signature and face together. The results show that the HOG feature descriptor significantly performs target matching at an average of 100% accuracy ratio for face recognition together with the digital signature. It outperforms existing feature sets with an accuracy of 84.25% for face only and 97.42% for digital signature only.
Highlights
Biometrics refer to personality check of people as indicated to their physical or behavioral qualities [1]
This study presents a new hyperactive system that depends on Histogram of Oriented Gradients (HOG) descriptor as features extraction for face recognition and digital signature together
Multi-biometric personal identification model using Histogram of Oriented Gradients (HOG) as feature extraction for face recognition and digital signature was present in this paper
Summary
Biometrics refer to personality check of people as indicated to their physical or behavioral qualities [1]. Numerous physical body parts and individual highlights have been utilizing for biometric Systems: faces, digital signatures, and DNA. Person verification in view of biometric highlights has pulled in more consideration in planning security systems [3]. Face recognition has as of late gotten critical consideration. It assumes a vital part in numerous application regions, for example, humanmachine communication, verification, and surveillance. Face recognition and digital signature have been a long-standing issue in PC vision [7]. Face and digital signature have been a long-standing problem in many applications, especially in computer vision. The proposed multibiometric model is based on HOG features of a face and digital signature and this model able to identify individuals accurately
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