Multi-task Cascaded Convolutional Networks Research Articles

GBCNN: A Full GPU-Based Batch Multi-Task Cascaded Convolutional Networks

Recently, the face detection and alignment is so popular and widely used in many research and application fields. Many superior face detection algorithms such as multi-task cascade convolutional network have been presented. However, it has difficulty in predicting faces among the big scale images in real time due to its three stages cascade architecture with less optimization. In this paper, we propose a full GPU-based batch multi-task cascade convolutional network which is carefully designed and optimized in each step to gain a superior speed performance. In addition, we present a novel parallel memory allocation strategy, which further enables our algorithm to support the batch operation, so that the system throughput increases significantly. In the experiment, our method achieves up to 300fps, over 600% speedup with an equal accuracy over the state-of-the-art methods on the face detection benchmarks.

A Deep Learning Approach for Face Detection and Location on Highway

Face detection and location technique is a hot research direction during recent years. Especially, driver face detection on highway is still a challenging problem in social safty deserving research. This paper proposes a novel algorithm based on the improved Multi-task Cascaded Convolutional Networks (MTCNN) and Support Vector Machine (SVM) to realize accurate face region detection and feature location of driver's face on highway, predicting face and feature location via a coarse-to-fine pattern. The proposed algorithm is verified under various complex highway conditions. Experimental results show that the proposed model shows satisfied performance compared to other state-of-the-art techniques used in driver face detection and alignment, keeping robust to the occlusions, varying pose and extreme illumination on highway.

Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this paper, we propose a deep cascaded multi-task framework which exploits the inherent correlation between them to boost up their performance. In particular, our framework adopts a cascaded structure with three stages of carefully designed deep convolutional networks that predict face and landmark location in a coarse-to-fine manner. In addition, in the learning process, we propose a new online hard sample mining strategy that can improve the performance automatically without manual sample selection. Our method achieves superior accuracy over the state-of-the-art techniques on the challenging FDDB and WIDER FACE benchmark for face detection, and AFLW benchmark for face alignment, while keeps real time performance.