Batch Image Processing in Facial Detection Applications

Abstract

Batch image processing for facial detection involves running a facial detection algorithm, in parallel, on batches containing multiple images rather than serially on a sequence consisting of single images. Batch image processing is crucial in live-video facial detection applications where the real-time processing of many frames is required. The performance of a facial detection application can be drastically improved when facial detection is done in parallel on batches containing multiple images. In this work, we analyze the performance gain due to running a GPU-based facial detection algorithm, in parallel, on batches of images versus the performance of running the GPU-based facial detection algorithm serially on a sequence of single images. We vary the number of images in which faces are to-be detected from 128 images to 1024 images. For each of the prior mentioned image sets, we measure the performance when the number of images per detection batch is varied from 1 image per batch (sequential) to 1024 images per batch in multiples of 2. We find that the technique of batch image processing improves the performance of a face detection application by approximately 10-11x on to-be-detected image sets consisting of 128, 256, and 1024 images. This performance improvement is attributed to a reduction in the communication overhead between the host CPU and GPU occurring on the PCI bus. Moreover, the technique of batch image processing enables the utilization of more of the GPU's resources that are left underutilized when GPU-based facial detection is done serially on a sequence of single images.