HFDSegNet: Holistic and Generalized Finger Dorsal ROI Segmentation Network

The aforementioned works and other analogous studies in finger knuckle images recognition have claimed that the precise detection of true features is difficult from poorly segmented images and the main reason for matching errors. Thus, an accurate segmentation of the region of interest is very crucial to achieve superior recognition results. In this paper, we have proposed a novel holistic and generalized segmentation Network (HFDSegNet) that automatically categorizes the given finger dorsal image obtained from multiple sensory resources into particular class and then extracts three possible ROIs (major knuckle, minor knuckle and nail) accurately. To best of our knowledge, this is the first attempt, an end-to-end trained object detector inspired by Deep Learning technique namely faster R-CNN (Region based Convolutional Neural Network) has been employed to detect and localize the position of finger knuckles and nail, even finger images exhibit blur, occlusion, low contrast etc. The experimental results are examined on two publicly available databases named as Poly-U contact-less FKI data-set, and Poly U FKP database. The proposed network is trained only over 500 randomly selected images per database, demonstrate the outstanding performance of proposed ROI’s segmentation network.