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- Title
Face Occlusion Detection Using Deep Convolutional Neural Networks.
- Authors
Xia, Yizhang; Zhang, Bailing; Coenen, Frans
- Abstract
With the rise of crimes associated with Automated Teller Machines (ATMs), security reinforcement by surveillance techniques has been a hot topic on the security agenda. As a result, cameras are frequently installed with ATMs, so as to capture the facial images of users. The main objective is to support follow-up criminal investigations in the event of an incident. However, in the case of miss-use, the user's face is often occluded. Therefore, face occlusion detection has become very important to prevent crimes connected with ATM usage. Traditional approaches to solving the problem typically comprise a succession of steps: localization, segmentation, feature extraction and recognition. This paper proposes an end-to-end facial occlusion detection framework, which is robust and effective by combining region proposal algorithm and Convolutional Neural Networks (CNN). The framework utilizes a coarse-to-fine strategy, which consists of two CNNs. The first CNN detects the head element within an upper body image while the second distinguishes which facial part is occluded from the head image. In comparison with previous approaches, the usage of CNN is optimal from a system point of view as the design is based on the end-to-end principle and the model operates directly on image pixels. For evaluation purposes, a face occlusion database consisting of over 50000 images, with annotated facial parts, was used. Experimental results revealed that the proposed framework is very effective. Using the bespoke face occlusion dataset, Aleix and Robert (AR) face dataset and the Labeled Face in the Wild (LFW) database, we achieved over 85.61%, 97.58% and 100% accuracies for head detection when the Intersection over Union-section (IoU) is larger than 0.5, and 94.55%, 98.58% and 95.41% accuracies for occlusion discrimination, respectively.
- Subjects
ARTIFICIAL neural networks; COMPUTER algorithms; FEATURE extraction; IMAGE analysis; DATABASES
- Publication
International Journal of Pattern Recognition & Artificial Intelligence, 2016, Vol 30, Issue 9, p-1
- ISSN
0218-0014
- Publication type
Article
- DOI
10.1142/S0218001416600107