In recent years, Face Image Quality Assessment (FIQA) plays an important role in the face recognition system. However, how to define face image quality is still an open question. In this work, we argue that a high-quality face image should have more identity-related information than a low-quality face image. Thus, we propose a novel unsupervised Face Image Quality Assessment with the variance of local contribution (VLC-FIQA). In our approach, we alternately mask partial pixels of the face image, then quantify the importance of these pixels and compute the variation of the importance of different parts as the quality of the image. Extensive experiments show that our VLC-FIQA outperforms state-of-the-art approaches on LFW. Our approach can be easily used for any recognition system and be extended to other recognition tasks such as person re-identification.

Motivation: The sequence alignment is a fundamental problem in bioinformatics. BLAST is a routinely used tool for this purpose with over 118 000 citations in the past two decades. As the size of bio-sequence databases grows exponentially, the computational speed of alignment softwares must be improved. Results: We develop the heterogeneous BLAST (H-BLAST), a fast parallel search tool for a heterogeneous computer that couples CPUs and GPUs, to accelerate BLASTX and BLASTP-basic tools of NCBI-BLAST. H-BLAST employs a locally decoupled seed-extension algorithm for better performance on GPUs, and offers a performance tuning mechanism for better efficiency among various CPUs and GPUs combinations. H-BLAST produces identical alignment results as NCBI-BLAST and its computational speed is much faster than that of NCBI-BLAST. Speedups achieved by H-BLAST over sequential NCBI-BLASTP (resp. NCBI-BLASTX) range mostly from 4 to 10 (resp. 5 to 7.2). With 2 CPU threads and 2 GPUs, H-BLAST can be faster than 16-threaded NCBI-BLASTX. Furthermore, H-BLAST is 1.5-4 times faster than GPU-BLAST. Availability and Implementation: https://github.com/Yeyke/H-BLAST.git. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.