Error-Tolerability Evaluation and Test for Images in Face Detection Applications
Tong-Yu Hsieh, Tai-Ang Cheng, Chao-Ru Chen · 2017
Face detection (i.e., checking if any face can be detected in a considered image) is expected to be one critical technology for IoT (Internet of Things). However, the images to be detected are likely to be erroneous when the image capture/storage/processing circuits are aged. Fortunately, for the face detection technology only a few details of an image are required for processing so as to make the detection process efficient. Thus high tolerability for image errors exists as long as the structure of the face is not destroyed too much. Well exploiting this feature will be very helpful to extend the lifetime of the face detection based system. However, no work in the literature evaluates the error-tolerability of such application. Most of the related error-tolerance work takes advantages of human beings' insensitivity to minor vibrations in multimedia signals. In this work we will show the error tolerability of images for the machine-based face detection application is even much larger than that for human based applications. One associated critical issue is therefore how to test if an erroneous image is still acceptable for face detection. Our analysis results show that typical image quality assessment methods would result in some misclassification. This motivates us to develop an efficient test method. The proposed method captures the incurred structural variances in an erroneous image, and evaluates if this variance is significant. The implementation of the proposed method is simple, which requires only addition, counting and comparison operations. Our experimental results on 3,438 erroneous benchmark images show that 99.57% test accuracy is achievable by the developed method.