Digital approach for lip prints analysis in Malaysian Malay population (Klang Valley): photograph on lipstick-cellophane tape technique

Personal identification in forensic investigation is not an easy process. Lip print analysis is one of the techniques that can be used to assist in human identification. This study was conducted to investigate the sex based on lip print pattern among Malaysian Malay population in Klang Valley, using photograph on lipstick-cellophane tape technique and the Suzuki and Tsuchihashi classification. A total of 360 subjects (180 males and 180 females) aged 15 and above were included in this study. The lip print was taken by pressing a cellophane tape to the lipstick applied on lip, pasted it onto a plain A4 paper and then photographed using a smart phone camera (OPPO F1). The images were then analysed using Adobe Photoshop software. A lip print image were divided into six sections: upper left, upper middle, upper right, lower right, lower middle and lower left. The Pearson chi-square test showed that there are significant differences between sexes in each section except for the upper middle section. Type V (irregular pattern) was the dominant pattern for the upper left, upper right, lower right and lower left sections (ranging from 71.1% to 86.7% for males and 80.6% to 83.9% for females) while type IV (reticular pattern) was mostly found in upper middle and lower middle section. Malaysian female displayed type V as the dominant lip print pattern in every section (ranging from 39.4% to 83.9%) except upper middle section and for Malaysian male, type V dominated the lip print pattern in all section (ranging from 71.1% to 86.7%) except for the upper and lower middle section. The result of this study can be applied in assisting the human identification for forensic science investigation

Lip print based authentication in physical access control Environments

Abstract: In modern society, there is an ever-growing need to determine the identity of a person in many applications including computer security, financial transactions, borders, and forensics. Early automated methods of authentication relied mostly on possessions and knowledge. Notably these authentication methods such as passwords and access cards are based on properties that can be lost, stolen, forgotten, or disclosed. Fortunately, biometric recognition provides an elegant solution to these shortcomings by identifying a person based on their physiological or behaviourial characteristics. However, due to the diverse nature of biometric applications (e.g., unlocking a mobile phone to cross an international border), no biometric trait is likely to be ideal and satisfy the criteria for all applications. Therefore, it is necessary to investigate novel biometric modalities to establish the identity of individuals on occasions where techniques such as fingerprint or face recognition are unavailable. One such modality that has gained much attention in recent years which originates from forensic practices is the lip. This research study considers the use of computer vision methods to recognise different lip prints for achieving the task of identification. To determine whether the research problem of the study is valid, a literature review is conducted which helps identify the problem areas and the different computer vision methods that can be used for achieving lip print recognition. Accordingly, the study builds on these areas and proposes lip print identification experiments with varying models which identifies individuals solely based on their lip prints and provides guidelines for the implementation of the proposed system. Ultimately, the experiments encapsulate the broad categories of methods for achieving lip print identification. The implemented computer vision pipelines contain different stages including data augmentation, lip detection, pre-processing, feature extraction, feature representation and classification. Three pipelines were implemented from the proposed model which include a traditional machine learning pipeline, a deep learning-based pipeline and a deep hybridlearning based pipeline. Different metrics reported in literature are used to assess the performance of the prototype such as IoU, mAP, accuracy, precision, recall, F1 score, EER, ROC curve, PR curve, accuracy and loss curves. The first pipeline of the current study is a classical pipeline which employs a facial landmark detector (One Millisecond Face Alignment algorithm) to detect the lip, SURF for feature extraction, BoVW for feature representation and an SVM or K-NN classifier. The second pipeline makes use of the facial landmark detector and a VGG16 or ResNet50 architecture. The findings reveal that the ResNet50 is the best performing method for lip print identification for the current study. The third pipeline also employs the facial landmark detector, the ResNet50 architecture for feature extraction with an SVM classifier. The development of the experiments is validated and benchmarked to determine the extent or performance at which it can achieve lip print identification. The results of the benchmark for the prototype, indicate that the study accomplishes the objective of identifying individuals based on their lip prints using computer vision methods. The results also determine that the use of deep learning architectures such as ResNet50 yield promising results.M.Sc. (Science