Improving the performance of the lip identification through the use of shape correction

This work presents automatic identification and verification approaches based on lip biometrics, using a static lip shape and applying a lip correction preprocessing, transforming data from Hidden Markov Model and being classified by Support Vector Machines. The classification system is conclusive for the identification of a person by the shape of the lips, even if the person presents soft facial emotions. Moreover the use of static lips shape has been revealed as a good option for security applications. The experiments have been carried out with two public datasets. One dataset was used to model and validate the approach, and the other dataset has been used to test the model blindly. The accuracy is up to 100% and 99.76% for GDPS-ULPGC and RaFD datasets respectively, using two training samples under a hold-out validation. Based on the results we can conclude that the system is very robust and stable with the highest classification capacity and minimal computation complexity.

[1]  Alan Wee-Chung Liew,et al.  Physiological and behavioral lip biometrics: A comprehensive study of their discriminative power , 2012, Pattern Recognit..

[2]  Suman Yadav,et al.  A comparative study of lip print patterns in monozygotic and dizygotic twins , 2017 .

[3]  Miguel A. Ferrer,et al.  Robust identification of persons by lips contour using shape transformation , 2010, 2010 IEEE 14th International Conference on Intelligent Engineering Systems.

[4]  Ruchi Sharma,et al.  Cheiloscopy: A Study of Morphological patterns of Lip Prints in Rajasthani population , 2015 .

[5]  José B. Mariño,et al.  Speech recognition in a noisy car environment based on LP of the one-sided autocorrelation sequence and robust similarity measuring techniques , 1997, Speech Commun..

[6]  Archana Alzapur,et al.  Lip prints- A study of its uniqueness among students of MediCiti Medical College. , 2017, Indian journal of clinical anatomy and physiology.

[7]  Miguel Angel Ferrer-Ballester,et al.  Offline geometric parameters for automatic signature verification using fixed-point arithmetic , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Lavlesh Kumar,et al.  Correlation between Fingerprint and Lip Print Pattern in Gujarati Population , 2017 .

[9]  R. Boostani,et al.  Lip segmentation in color images , 2008, 2008 International Conference on Innovations in Information Technology.

[10]  Chi-Ho Chan,et al.  Local Ordinal Contrast Pattern Histograms for Spatiotemporal, Lip-Based Speaker Authentication , 2012, IEEE Trans. Inf. Forensics Secur..

[11]  Anupam Shukla,et al.  Expert System for Speaker Identification Using Lip Features with PCA , 2010, 2010 2nd International Workshop on Intelligent Systems and Applications.

[12]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[13]  Stephen J. Cox,et al.  Speaker independent visual-only language identification , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  Miguel A. Ferrer,et al.  gpdsHMM : A HIDDEN MARKOV MODEL TOOLBOX IN THE MATLAB ENVIRONMENT , 2004 .

[15]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[16]  Rafal Doroz,et al.  Using a Probabilistic Neural Network for lip-based biometric verification , 2017, Eng. Appl. Artif. Intell..

[17]  Jesús B. Alonso,et al.  Using a Discrete Hidden Markov Model Kernel for lip-based biometric identification , 2014, Image Vis. Comput..

[18]  Jagdish Lal Raheja,et al.  Facial Gesture Identification Using Lip Contours , 2010, 2010 Second International Conference on Machine Learning and Computing.

[19]  Jean-Philippe Thiran,et al.  Automatic Extraction of Geometric Lip Features with Application to Multi-Modal Speaker Identification , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[20]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[21]  Skyler T. Hawk,et al.  Presentation and validation of the Radboud Faces Database , 2010 .

[22]  Miguel A. Ferrer,et al.  Biometric identification system by lip shape , 2002, Proceedings. 36th Annual 2002 International Carnahan Conference on Security Technology.

[23]  David Haussler,et al.  A Discriminative Framework for Detecting Remote Protein Homologies , 2000, J. Comput. Biol..

[24]  Xiaoou Li,et al.  Support vector machine classification for large data sets via minimum enclosing ball clustering , 2008, Neurocomputing.

[25]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[26]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[27]  Miguel Angel Ferrer-Ballester,et al.  Bimodal biometric verification based on face and lips , 2011, Neurocomputing.

[28]  Stephen J. Cox,et al.  Automatic visual-only language identification: A preliminary study , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[29]  Ruchi Sharma,et al.  LIP PRINT PATTERNS IN RAJASTHANI POPULATION , 2015 .

[30]  Edward R. Dougherty,et al.  Is cross-validation valid for small-sample microarray classification? , 2004, Bioinform..