Lee Ming Cheng

The use of human biometrics is becoming widespread and their major application is human recognition for controlling unauthorized access to both digital services and physical localities. However, the practical deployment of human biometrics for recognition poses a number of challenges, such as template storage capacity, computational requirements, and privacy of biometric information. These challenges are important considerations, in addition to performance accuracy, especially for authentication systems with limited resources. In this paper, we propose wave atom transform (WAT) based palm-vein recognition scheme. The scheme computes, maintains, and matches palm-vein templates with less computational complexity and less storage requirements under secure and privacy-preserving environment. First, we extract palm-vein traits in the WAT domain, which offers sparser expansion and better capability to extract texture features. Then, randomization and quantization are applied to the extracted features to generate a compact, privacy-preserving palm-vein template. We analyze the proposed scheme for its performance and privacy-preservation. The proposed scheme obtains equal error rates (EER) of 1.98%, 0%, 3.05%, and 1.49 for PolyU, PUT, VERA and our palm-vein datasets, respectively. The extensive experimental results demonstrate comparable matching accuracy of the proposed scheme with a minimum template size and computational time of 280 bytes and 0.43 seconds, respectively.

There is a noticeable tendency to apply deep convolutional neural network (CNN) in facial identification, since it is able to boost performance in face recognition and verification. However, due to the users have unique facial, exposure of face template to adversaries can severely compromise system security and users’ privacy. Here, the authors propose a face template protection technique by using multi‐label learning, which maps the facials into low‐density parity‐check (LDPC) codes. Firstly, a random binary sequence is generated to represent a user and further hashed to produce the protected template. During the training, the random binary sequences are encoded by an LDPC encoder to produce diverse binary codes. Based on carefully designed deep multi‐label learning, the facial features of each user are mapped to a diverse binary code. In the process of recognition and verification, the deep CNN mapping architecture is modelled as a Gaussian channel, while the noise brought by intra‐variations in the outputs of CNN can be removed by the LDPC decoder. Thus, a robust face template protection scheme is achieved. The simulation results on PIE and extended Yale B indicate that the proposed scheme achieves high genuine accept rate at 1% false accept rate.

The design of unified, efficient and lightweight cryptographic platform for resource-constrained on-board devices such as sensors, microcontrollers and actuators in the context of Internet of Vehicles (IoV) remains an open and challenging problem, for both academic and industry. Elliptic Curve Cryptography (ECC) is considered as a promising encryption algorithm for the next generation communications, as it could provide the same strong security level using relatively smaller key size when compared to the currently used RSA algorithm. However, traditional ECCs have the disadvantage of using a fixed curve, making it very easy to be intensively analyzed while being hard to construct a united platform for on-board devices with processors of different instruction lengths. To mitigate the above problem, this paper suggests a dynamic scalable elliptic curve cryptosystem. To synchronize the curve in use, a curve list of different security levels is generated and preserved on both parties. Since both parties randomly choose the curve and the prime number, a extra security level could be provided, so that the security level can still remain the same even using smaller key sizes, while the computation efficiency will be enhanced and the power consumption will be reduced, which is especially suitable for the application in on-board embedded devices. Detailed experimental results illustrate that the presented scheme improves the efficiency by 30% in average when compared with traditional ECC implementations on a similar security level. Therefore, the proposed scalable ECC scheme as a unified cryptographic platform is more economic for these on-board devices in vehicles.