A Novel Cancelable FaceHashing Technique Based on Non-Invertible Transformation With Encryption and Decryption Template

Abstract

A novel cancelable FaceHashing technique based on non-invertible transformation with encryption and decryption template has been proposed in this paper. The proposed system has four components: face preprocessing, feature extraction, cancelable feature extraction followed by the classification, and encryption/decryption of cancelable face feature templates. During face preprocessing, the facial region of interest has been extracted out to speed the process for evaluating discriminant features. In feature extraction, some optimization techniques such as Sparse Representation Coding, Coordinate descent, and Block coordinates descent have been employed on facial descriptors to obtain the best representative of those descriptors. The representative descriptors are further arranged in a spatial pyramid matching structure to extract more discriminant and distinctive feature vectors. To preserve them, the existing BioHashing technique has been modified and extended to some higher levels of security attacks and the modified BioHashing technique computes a cancelable feature vector by the combined effect of the facial feature vector and the assigned token correspond to each user. The elements of computed cancelable feature vector are in a numeric form that has been employed to perform both verifications as well as identification task in online while the original facial feature vectors are kept offline either in hard drive or disc. Then, to enhance more security levels and also to preserve the cancelable face features, an RSA based encryptiondecryption algorithm has been introduced. The proposed system has been tested using four benchmark face databases: CASIA-FACE-v5, IITK, CVL, and FERET, and performance are obtained as correct recognition rate and equal error rate. The performance are compared to the state-of-the-art methods for the superiority of the proposed feature extraction technique and individual performance analysis has been performed at all the security levels of the proposed Cancelable FaceHashing Technique. These comparisons show the superiority of the proposed system.

Stable Hash Generation for Efficient Privacy-Preserving Face Identification

“…According to Drozdowski et al [11], only a few works have combined WR strategies with BTP schemes to build large-scale identification systems that ensure privacy protection. In the context of face biometrics, those studies have mainly employed cancelable biometrics [19]- [21]. However, most of those systems still report a degradation w.r.t.…”

“…In contrast, the approach in [19] achieved competitive results with respect to its baseline (i.e., unprotected system) by employing different fusion strategies. Inspired by this idea, Sardar et al [21] introduced a novel hashing technique, namely FaceHashing. Within this method, privacy protection is achieved by modifying the BioHashing technique [39], [40].…”

“…In addition, not all methods have properly shown, that they can reach unlinkability for cross-comparison attacks [15]. Further, WR strategies employed by these systems (e.g., [19]- [21]) are limited to feature transformation or exhaustive pre-selection approaches (e.g., [25]).…”

PlexNet: An Ensemble of Deep Neural Networks for Biometric Template Protection

“…Sardar et al, proposed a hybrid solution by combining the non-invertibility with a biocode encryption. This helped in enhancing security levels along with better accuracy [39]. Talreja et al…”

Chaotic Cryptosystem for Selective Encryption of Faces in Photographs

“…en, to provide greater security, they adapt the RSA algorithm [13]. Asgari-Chenaghlu et al use YoloV3 to automatically detect objects or the human body of the ROI delimiter and utilize a cryptosystem based on the chaotic sine map and logistic map systems.…”