Improving automated latent fingerprint detection and segmentation using deep convolutional neural network
Neural Computing and Applications
Latent fingerprint segmentation is a complex process of separating relevant areas called fingerprints from an irrelevant background in the latent fingerprint image which is of poor quality. A breakthrough in the field can be used to segment fingerprints accurately from the background by using optimal resources. Processing of unwanted background of the entire image can lead to false and missed detection of fingerprints. An early fingerprint distinction technique based on colour and saliency masks is proposed to detect potentially relevant areas out of the entire image area for further processing, using a non-learning approach. Later, the patches of early detected fingermarks are fed to a stacked convolutional autoencoder for separating imposters of fingerprint(s) region from relevant fingerprint(s) regions, using a deep learning approach. The inspiration to use the convolutional neural network in this hybrid approach is to effectively capture feature distinction from potential features similar to that of object detection and classification. The inspiration to use autoencoder in a stack is to provide better feature engineering for CNN. The use of the pre-trained convolutional neural network with a stack of autoencoders for image classification and segmentation produces better results than a naive convolutional neural network. The experiments are conducted on the IIIT-D database. The efficiency and effectiveness of the model over good quality images is evaluated by experimenting over different patch sizes, with and without the use of dropout in CNN, with and without use of Autoencoder with CNN. The early detection of contours along with patch-based classification-cum-segmentation using SCAE on good quality images produces 98.45% segmentation accuracy.
Open Access Status
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