An Accurate Multi-Biometric Personal Identification Model Using Histogram Of Oriented Gradients (Hog)

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.