Lip Print Recognition Algorithm Based on Convolutional Network

Abstract

Identity information security is faced with various challenges, and the traditional identification technology cannot meet the needs of public security. Therefore, it is necessary to further explore and study new identification technologies. In order to solve the complex image preprocessing problems, difficult feature extraction by artificial design algorithm, and low accuracy of lip print recognition, a method based on the convolutional neural network is proposed, by building a convolutional neural network called LPRNet (Lip Print Recognition Network). The obtained lip print image is inputted into the training recognition model of the network to simplify the lip print image preprocessing. By extracting feature information and sampling operation, the model training parameters are reduced, which overcomes the difficulty of designing a complex algorithm to extract features. By analyzing and comparing the experimental results, a higher recognition rate is obtained, and the validity of the method is verified.

Evaluating Digital Photography for Lip Print Recording: Compatibility With Traditional Classification Systems

“…The conventional cellophane tape-lipstick method has limitations in real-world applications. The quality of the obtained image is subjective and depends on factors like pressure and direction applied during the process [ 16 ]. Quality is crucial for classification using existing systems.…”

“…The primary concern with the direct digital photographic method is the difficulty in capturing the three-dimensional structure and intricate details of the lips [ 4 ]. Exploring alternative methods for lifting lip prints from various surfaces is warranted [ 16 ]. For effective analysis, a digital scoring system on digital images, as recommended by Prabhu et al, is advisable for building databases or implementing sorting systems [ 4 ].…”