Personal identification utilizing lip print furrow based patterns. A new approach.

This paper proposes a new method of personal identification that introduces the analysis of lip prints. In spite of its important role in forensic and biometric applications, the results of previous investigations into lip prints are scanty. This is mainly due to the difficulties that accompany any analysis of the lips: lips are very flexible and pliable, and successive lip print impressions – even those obtained from the same person – may significantly differ from one other. Our article’s principal contribution is a proposal for a new personal identification methodology and application that uses, for the first time, a strategy for biometric and forensic analysis of lip print structure. As a result of our analysis we propose a lip print pattern for each individual. This pattern contains only such furrows that occur on the greatest number of lip prints obtained from the same person, where these furrows’ locations and inclinations remain similar across the lip prints obtained. It should be noted that in our approach, instead of lip photos we employ lip prints, such as can be obtained at a crime scene. It is worth noticing also that we propose a new method of personal identification where, instead of popular machine learning methods,the furrow-analysis of lip prints is introduced. Thus, no classifier learning phase is required. According to the authors’ convictions, based on reports in the literature, the proposed approach describes for the first time a strategy as to how lip print structures could be analyzed in biometric applications. The main novelty of this paper is its use of multiple lip print furrows from the same person to determine the most common lip furrow distribution in four different directions. On the basis of the pattern recognition of the lip furrows, the identity of the person will be determined. The proposed method’s effectiveness is 92.73%, determined using a database containing 350 lip prints. [ABSTRACT FROM AUTHOR]