Improvement of Face and Eye Detection Performance by Using Multi-task Cascaded Convolutional Networks

Detection of face and eyes in unrestricted conditions has been a problem for years due to various expressions, illumination, and color fringing. Recent studies show that deep learning methods can attain impressive performance in the identification of different objects and patterns. As various systems may use the human face as input material, the increase in facial and eye detection performance has some significance. This paper introduces an enhanced face and eye detection technique through the use of cascaded multi-task convolutional networks for our dataset. We propose in this paper a deep cascaded multi-task system that exploits their inherent correlation to improve their performance. We collected 100 videos containing about 18265 images captured from our device and applied this dataset to the process and other systems proposed. The educated model was checked on our dataset and contrasted with the Haar cascade model as well. Our proposed method achieves a 98% percent accuracy rate considering our dataset which is superior to the other techniques used to detect the face and eye from an image. Besides, this paper also reflects a study of different methods of detecting the eye and face in tabular format from videos. The experimental results however indicate that the proposed approach demonstrates enhanced eye and face detection output from videos.