دانلود رایگان مقاله انگلیسی قدرت و ضعف مدل های یادگیری عمیق برای تشخیص چهره در برابر تخریب تصویر - IEEE 2018

Abstract:

Convolutional neural network (CNN) based approaches are the state of the art in various computer vision tasks including face recognition. Considerable research effort is currently being directed toward further improving CNNs by focusing on model architectures and training techniques. However, studies systematically exploring the strengths and weaknesses of existing deep models for face recognition are still relatively scarce. In this paper, we try to fill this gap and study the effects of different covariates on the verification performance of four recent CNN models using the Labelled Faces in the Wild dataset. Specifically, we investigate the influence of covariates related to image quality and model characteristics, and analyse their impact on the face verification performance of different deep CNN models. Based on comprehensive and rigorous experimentation, we identify the strengths and weaknesses of the deep learning models, and present key areas for potential future research. Our results indicate that high levels of noise, blur, missing pixels, and brightness have a detrimental effect on the verification performance of all models, whereas the impact of contrast changes and compression artefacts is limited. We find that the descriptor-computation strategy and colour information does not have a significant influence on performance.

5 Conclusion

We have presented a systematic study of covariate effects on face verification performance of four recent deep CNN models. We observe that the studied models are affected by image quality to different degrees, but all of them degrade in performance quickly and significantly, when evaluated on lower-quality images than they were trained with. However, given proper architecture choices and training procedures, a deep learning model can be made relatively robust to common sources of image-quality degradations. We found that the models considered were the most easily and consistently degraded in performance through image blurring, which is similar in nature to real-life scenarios of attempting face recognition from low-resolution imagery. Other covariates found to have a considerable effect on the verification performance were noise, image brightness, and missing data, while image contrast  and JPEG compression impacted the performance of the models only marginally. Most of the models considered were least affected by changes in input colour space – despite being trained on full colour images – their performance drops negligibly when evaluated on grey-scale images. This finding is also corroborated by the results of the contrast experiments. No specific architecture was found to be significantly more robust than others to all covariates. The VGG-Face model, for example, was most robust to noise, but performed least well for changes in image brightness. GoogLeNet, on the other hand, performed worst on noise and image blur, but had a slight advantage over the remaining models with images of reduced contrast.