Hybrid HOG-SVM encrypted face detection and recognition model. (2nd January 2022)

• Record Type: Journal Article
• Authors: Sharma, Shilpa; Raja, Linesh; Bhatnagar, Vaibhav; Sharma, Divya; Bhagirath, Swami Nisha; Poonia, Ramesh Chandra
• Is Part Of: Journal of discrete mathematical sciences & cryptography. Volume 25:Number 1(2022)
• Page Start: 205
• Page End: 218
• Publication Date: 2022-01-02
• Subjects: 68M25; HOG -- SVM -- DES3 -- RSA -- Face landmark estimation; Computer science -- Mathematics -- Periodicals; Cryptography -- Periodicals; Computer science -- Mathematics; Cryptography; Periodicals; 004.0151
• DOI: 10.1080/09720529.2021.2014141
• Languages: English
• ISSNs: 0972-0529

Abstract

Abstract: Security plays a major role in an individual's life to win this world with highly secure and authentic lifestyle with the digital equipment's. The paper proposed an encryption based secure face detection and recognition model which can be implemented in daily life to generate a more robust and efficient security bubble around the world. The most crucial problem encountered during face recognition is due to the variation in face direction of an individual, the model solves the mentioned pose variation problem. The proposed model takes the help of face recognition library to recognize the face and use HOG (Histogram of Oriented Gradients) & SVM for checking the face authentication by performing an image match, the model also applies the concept of HOG to generate the encoded features from the image. The system is divided into two modules first is to detect a face and then match the detected face from the authentic persons dataset available. The system uses the concept of OpenCV library for giving a support system for the real time image. For data encryption, proposed model used the concept of DES3 and RSA algorithm. The proposed model gets 83.33% accuracy while tested for three different image types and states that the RSA algorithm performs encryption in less computational time.