A Novel Cancelable FaceHashing Technique Based on Non-invertible Transformation with Encryption and Decryption Template

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 encryption-decryption 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

Enhanced biometric template protection schemes for securing face recognition in IoT environment

With the increasing use of biometrics in Internet of Things (IoT) based applications, it is essential to ensure that biometric-based authentication systems are secure. Biometric characteristics can be accessed by anyone, which poses a risk of unauthorized access to the system through spoofed biometric traits. Therefore, it is important to implement secure and efficient security schemes suitable for real-life applications, less computationally intensive, and invulnerable. This work presents a hybrid template protection scheme for secure face recognition in IoT-based environments, which integrates Cancelable Biometrics and Bio-Cryptography. Mainly, the proposed system involves two steps: face recognition and face biometric template protection. The face recognition includes face image preprocessing by the Tree Structure Part Model (TSPM), feature extraction by Ensemble Patch Statistics (EPS) technique, and user classification by multi-class linear support vector machine (SVM). The template protection scheme includes cancelable biometric generation by modified FaceHashing and a Sliding-XOR (called S-XOR) based novel Bio-Cryptographic technique. A user biometric-based key generation technique has been introduced for the employed Bio-Cryptography. Three benchmark facial databases, CVL, FEI, and FERET, have been used for the performance evaluation and security analysis. The proposed system achieves better accuracy for all the databases of 200-dimensional cancelable feature vectors computed from the 500-dimensional original feature vector. The modified FaceHashing and S-XOR method shows superiority over existing face recognition systems and template protection

Electrical Characterization and Doping Uniformity Measurement during Crystalline Silicon Solar Cell Fabrication Using Hot Probe Method

The parameters of crystalline semiconductor such as types of semiconductor, uniformity of impurity concentration of doped wafer, majority charge carrier concentration, sheet resistivity of doped wafer surface play an important role in solar cell fabrication process during emitter diffusion, that is the most critical step. In this paper, we have used a low cost in house made hot probe measurement setup. A hot plate was used to heat up the wafer up to 100°C. Two k-type thermocouples were placed simultaneously in contact with the hot and cold surface of the wafer to measure the temperature in situ for both hot and cold probe. We have used two copper probes with a voltmeter connected to measure the potential difference (thermoelectric voltage) between two probes for various temperatures up to 100°C with an interval of 10°C. We have taken measurement for commercial silicon wafer (thickness 200 µm) and one side polished 4 inch diameter Si wafer (thickness 660 µm) to determine the wafer type (n-type or p-type). We also calculated thermo-power or Seebeck coefficient from the voltage vs. time curve, that is constant for particular substrate. As a process monitoring tool for solar cell fabrication process, after n-type diffusion using POCl3 on p-type silicon wafer of thickness 200 µm, we have done wafer mapping that gives us the information of doping uniformity over the whole surface of wafer both front and back side  &nbsp