In certain applications, fingerprint authentication systems require templates to be stored in databases. The possible leakage of these fingerprint templates can lead to serious security and privacy threats. Therefore, with the frequent use of fingerprint authentication on mobile devices, it has become increasingly important to keep fingerprint data safe. Due to rapid developments in optical equipment, biometric systems are now able to gain the same biometric images repeatedly, so strong features can be selected with precision. Strong features refer to high-quality features which can be easily distinguished from other features in biometric raw images. In this paper, we introduce an algorithm that identifies these strong features with certain probability from a given fingerprint image. Once values are extracted from these features, they are used as the authentication data. Using the geometric information of these strong features, a cancelable fingerprint template can be produced, and the process of extracting values and geometric information is further explained. Because this is a light-weight authentication scheme, this template has practical usage for low performance mobile devices. Finally, we demonstrate that our proposed schemes are secure and that the user’s biometric raw data of the fingerprint are safe, even when the mobile device is lost or stolen.
We present AB9, a neural processor for inference acceleration. AB9 consists of a systolic tensor core (STC) neural network accelerator designed to accelerate artificial intelligence applications by exploiting the data reuse and parallelism characteristics inherent in neural networks while providing fast access to large on‐chip memory. Complementing the hardware is an intuitive and user‐friendly development environment that includes a simulator and an implementation flow that provides a high degree of programmability with a short development time. Along with a 40‐TFLOP STC that includes 32k arithmetic units and over 36 MB of on‐chip SRAM, our baseline implementation of AB9 consists of a 1‐GHz quad‐core setup with other various industry‐standard peripheral intellectual properties. The acceleration performance and power efficiency were evaluated using YOLOv2, and the results show that AB9 has superior performance and power efficiency to that of a general‐purpose graphics processing unit implementation. AB9 has been taped out in the TSMC 28‐nm process with a chip size of 17 × 23 mm2. Delivery is expected later this year.
Convolutional Neural Networks (CNN) have been successfully employed in the field of image classification. However, CNN trained using images from several years ago may be unable to identify how such images have changed over time. Cross-age face recognition is, therefore, a substantial challenge. Several efforts have been made to resolve facial changes over time utilizing recurrent neural networks (RNN) with CNN. The structure of RNN contains hidden contextual information in a hidden state to transfer a state in the previous step to the next step. This paper proposes a novel model called Hidden State-CNN (HSCNN). This adds to CNN a convolution layer of the hidden state saved as a parameter in the previous step and requires no more computing resources than CNN. The previous CNN-RNN models perform CNN and RNN, separately and then merge the results. Therefore, their systems consume twice the memory resources and CPU time, compared with the HSCNN system, which works the same as CNN only. HSCNN consists of 3 types of models. All models load hidden state h t−1 from parameters of the previous step and save h t as a parameter for the next step. In addition, model-B adds h t−1 to x, which is the previous output. The summation of h t−1 and x is multiplied by weight W. In model-C the convolution layer has two weights: W 1 and W 2 . Training HSCNN with faces of the previous step is for testing faces of the next step in the experiment. That is, HSCNN trained with past facial data is then used to verify future data. It has been found to exhibit 10 percent greater accuracy than traditional CNN with a celeb face database.
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