Beyond Disentangled Representations: An Attentive Angular Distillation Approach to Large-scale Lightweight Age-Invariant Face Recognition

Disentangled representations have been commonly adopted to Age-invariant Face Recognition (AiFR) tasks. However, these methods have reached some limitations with (1) the requirement of large-scale face recognition (FR) training data with age labels, which is limited in practice; (2) heavy deep network architecture for high performance; and (3) their evaluations are usually taken place on age-related face databases while neglecting the standard large-scale FR databases to guarantee its robustness. This work presents a novel Attentive Angular Distillation (AAD) approach to Large-scale Lightweight AiFR that overcomes these limitations. Given two high-performance heavy networks as teachers with different specialized knowledge, AAD introduces a learning paradigm to efficiently distill the age-invariant attentive and angular knowledge from those teachers to a lightweight student network making it more powerful with higher FR accuracy and robust against age factor. Consequently, AAD approach is able to take the advantages of both FR datasets with and without age labels to train an AiFR model. Far apart from prior distillation methods mainly focusing on accuracy and compression ratios in closed-set problems, our AAD aims to solve the open-set problem, i.e. large-scale face recognition. Evaluations on LFW, IJB-B and IJB-C Janus, AgeDB and MegaFace-FGNet with one million distractors have demonstrated the efficiency of the proposed approach. This work also presents a new longitudinal face aging (LogiFace) database for further studies in age-related facial problems in future.Comment: arXiv admin note: substantial text overlap with arXiv:1905.1062

Non-surjective finite alphabet iterative decoders

International audienceThis paper introduces a new theoretical framework, akin to the use of imprecise message storage in Low Density Parity Check (LDPC) decoders, which is seen as an enabler for cost-effective hardware designs. The proposed framework is the one of Non-Surjective Finite Alphabet Iterative Decoders (NS-FAIDs), and it is shown to provide a unified approach for several designs previously proposed in the literature. NS-FAIDs are optimized by density evolution for WiMAX irregular LDPC codes and we show they provide different trade-offs between hardware complexity and decoding performance. In particular, we derive a set of 27 NS-FAIDs that provide decoding gains up to 0.36 dB, while yielding a memory / interconnect reduction up to 25% / 30% compared to the Min-Sum decoder

FPGA design of high throughput LDPC decoder based on imprecise Offset Min-Sum decoding

International audienc

Pipelined digital filters and their applications:fdatool design and verilog HDL verification

Abstract This research will provide system on chip design for pipelined digital filters module. Two basic but important FIR and IIR filters are going to be discussed. At first, the position of digital filters in digital system is explained. Then, MATLAB fdatool and scripts are used for filter design. Finally, the implementation and verification of proposed filter processor are performed VERILOG hardware description language (HDL).All scripts, algorithm is clearly given. We hope that the research will be a great reference and an intellectual property core for engineers and researcher students