In this paper, the problem of Palmprint Recognition Across Different Devices (PRADD) is investigated, which has not been well studied so far. Since there is no publicly available PRADD image database, we created a non-contact PRADD image database containing 12,000 grayscale captured from 100 subjects using three devices, i.e., one digital camera and two smart-phones. Due to the non-contact image acquisition used, rotation and scale changes between different images captured from a same palm are inevitable. We propose a robust method to calculate the palm width, which can be effectively used for scale normalization of palmprints. On this PRADD image database, we evaluate the recognition performance of three different methods, i.e., subspace learning method, correlation method, and orientation coding based method, respectively. Experiments results show that orientation coding based methods achieved promising recognition performance for PRADD.

An extensive thermodynamic investigation of the Al – Ni – Si system is carried out via an integrated approach of calculation of phase diagrams, first-principles calculations, and key experiments. Eighteen decisive alloys are prepared in order to verify the existence of the previously reported ternary compounds and to provide new phase equilibrium data. Phase compositions, microstructure, and phase transition temperatures are determined using the combined techniques of X-ray diffraction, scanning electron microscopy, energy dispersion X-ray analysis, and differential thermal analysis. The order/disorder transition between disordered bcc_A2 and ordered bcc_B2 phases as well as that between disordered fcc_A1 and ordered L12 phase are described using a two-sublattice model. A self-consistent parameter set is finally obtained by considering the huge amount of experimental data including 13 vertical sections and 5 isothermal sections from both the literature and the present experiments. Almost all of the reliable phase diagram data can be well described by the present modeling. The reliability of the calculated thermodynamic properties for ternary phases is verified through enthalpy measurement employing drop calorimetry and first-principles calculations. The thermodynamic parameters obtained can also successfully predict most of the thermodynamic properties and describe the solidification path for the selected as-cast alloy Al6Ni55Si39.

This paper mainly apply Da Vinci digital media processor DM6446 designed by TI Company as Central Processing Unit, which internally integrated dual-core, namely, ARM and DSP, it utilizes the high-speed data processing capacity of DSP for the operational processing of face recognition algorithm and performs contrast recognition and extraction of the face image characteristics, so as to provide a stable and effective operating control and interface support.