Lip Print Recognition Based on Mean Differences Similarity Measure

Smacki, Lukasz; Wróbel, Krzysztof

doi:10.1007/978-3-642-20320-6_5

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…20 samples of lip prints for 4 people are used and author reported 91 to 96 percent efficiency. The core of method in [11] is Hough transform to detect lines on the lips. As a result, a collection of line segments approximating the lip print pattern is obtained.…”

Section: Related Workmentioning

confidence: 99%

A Hidden Markov Model (HMM) Scheme for Lip based Identification Utilizing Vertical Grooves Angles

Mousavi¹,

Zarrabi²

2018

IJCA

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As the lips move too quickly, the shape of lips images may differ from one situation to another for an individual such as the cases of smiling or frowning. In this paper, we propose a novel approach to automatic classification of lips images which are captured by digital camera. We focus on the lower part of lips based on morphological features of the grooves. Image processing techniques are employed to replace the traditional lipprints to detect human identification with intelligent systems. Presented methodology takes lower part of the lips as an input to process their orientation of grooves. Morphological and structural features are considered to describe the lips. A Hidden Markov Model is proposed for classification and training. Results show the proposed algorithm is robust against scale changes, noises and multiple orientations of lip images for each person. With this approach 93.4%, 88.3% and 85.1% accuracy is achieved in classification of 5, 10, 15 class respectively on 75 lip samples. Presented algorithm may contribute significantly for the development of applications related to human identifications or can be used as a supplement to lip identification systems (for example, methods which are using contour of the lips) to reduce the error rate of classification.

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Section: Related Workmentioning

confidence: 99%

A Hidden Markov Model (HMM) Scheme for Lip based Identification Utilizing Vertical Grooves Angles

Mousavi¹,

Zarrabi²

2018

IJCA

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“…For this reason, the lip print image, I, should be enhanced and all of its artefacts removed. Many lip print feature extraction algorithms have been reported in the literature [1,[6][7][8][9][10][11][12][13]. In this paper, a new approach to lip print extraction and analysis is presented.…”

Section: Lip Print Pre-processingmentioning

confidence: 99%

“…This is the method of capture that we utilized during our investigations, as it allows for the lip images to capture more precise textures. These techniques have been described in a number of papers [2,[6][7][8][9][10][11][12][13]. The recognition results reported in the aforementioned papers have now been significantly improved.…”

Section: Introductionmentioning

confidence: 99%

DTW and Voting-Based Lip Print Recognition System

Porwik¹,

Orczyk²

2012

Computer Information Systems and Industrial Management

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Abstract. This paper presents a method of lip print comparison and recognition. In the first stage the appropriate lip print features are extracted. Lip prints can be captured by police departments. Traces from lips may also found at a crime scene. The approach uses the well known DTW algorithm and Copeland vote counting method. Tests were conducted on 120 lip print images. The results obtained are very promising and suggest that the proposed recognition method can be introduced into professional forensic identification systems.

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“…Lukasz Smacki has also done significant research studying the groove patterns in the human lips for personal identification [11]. He has also proposed a method of lip print identification using DTW algorithm [12].…”

Section: Review Workmentioning

confidence: 99%

Personal Identification from Lip-Print Features using a Statistical Model

Bhattacharjee¹,

Arunkumar²,

Bandyopadhyay³

2012

IJCA

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This paper presents a novel approach towards identification of human beings from the statistical analysis of their lip prints. Lip features are extracted by studying the spatial orientations of the grooves present in lip prints of individuals using standard edge detection techniques. Horizontal, vertical and diagonal groove features are analysed using connectedcomponent analysis to generate the region-specific edge datasets. Comparison between test and reference sample datasets against a threshold value to define a match yield satisfactory results. FAR, FRR and ROC metrics have been used to gauge the performance of the algorithm for real-world deployment in unimodal and multimodal biometric verification systems.

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Lip Print Recognition Based on Mean Differences Similarity Measure

Cited by 7 publications

References 7 publications

A Hidden Markov Model (HMM) Scheme for Lip based Identification Utilizing Vertical Grooves Angles

A Hidden Markov Model (HMM) Scheme for Lip based Identification Utilizing Vertical Grooves Angles

DTW and Voting-Based Lip Print Recognition System

Personal Identification from Lip-Print Features using a Statistical Model

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