Person identification from lip texture analysis

The interactive liveness detection for fact recognition often requires users to read some digits from 0 to 9. The movement and variation of lip texture during reading potentially provide discriminative information for human identification. This paper firstly addressed the issue of whether the lip texture during reading can serve as a soft-biometric for person identification. Different from the traditional lip recognition methods that are based on color statistics and lip shapes, we develop a deep architecture that incorporates both CNN and LSTM to jointly model the appearance and the spatial-temporal information of lip texture. We also build a new lip recognition database that contains 11,123 videos for the number 0∼9 in Chinese from 57 people. Experimental results show that the proposed method can achieve 96.01% on close-set protocols, suggesting the usage of lip texture as soft-biometrics for facilitating face recognition.

[1]  Dmitry O. Gorodnichy,et al.  Video-based framework for face recognition in video , 2005, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05).

[2]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[3]  Sharath Pankanti,et al.  Biometrics: Personal Identification in Networked Society , 2013 .

[4]  Wojciech Zaremba,et al.  Learning to Execute , 2014, ArXiv.

[5]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[9]  David J. Kriegman,et al.  Video-based face recognition using probabilistic appearance manifolds , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Ran He,et al.  Locally imposing function for Generalized Constraint Neural Networks - A study on equality constraints , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[11]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[12]  Zhenan Sun,et al.  A Lightened CNN for Deep Face Representation , 2015, ArXiv.

[13]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Larry S. Davis,et al.  Learning predictable binary codes for face indexing , 2015, Pattern Recognit..

[15]  Masakazu Matsugu,et al.  Subject independent facial expression recognition with robust face detection using a convolutional neural network , 2003, Neural Networks.

[16]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Rong-yi Cui,et al.  Video-Based Recognition of Walking States , 2011, ICAIC.

[18]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[19]  David Zhang,et al.  Automated Biometrics: Technologies and Systems , 2000 .

[20]  Zhenan Sun,et al.  Multi-task ConvNet for blind face inpainting with application to face verification , 2016, 2016 International Conference on Biometrics (ICB).

[21]  Alex Graves,et al.  Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.

[22]  Fernando De la Torre,et al.  Supervised Descent Method and Its Applications to Face Alignment , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.