A Racial Recognition Method Based on Facial Color and Texture for Improving Demographic Classification

Facial recognition is one of the important techniques in the security and authentication domain of the present time. Facial image recognition involves complex process which reduces the overall performance of the system for a large database, and consequently, it may incur inefficiency to the system in the commercial sector. In this paper, we split the image database into a set of smaller groups by classifying the face images in terms of race demography. First, facial components (i.e., eyes, nose and mouth) are captured using a segmentation technique and then race sensitive features: chromatic/skin tone and local features from face images are extracted using Color Coherence Vector and Gabor filter. K-Nearest Neighbors, Artificial Neural Network, and Support Vector Machines are then used to classify the face image according to race groups. We consider racial classification as Asian, African and European. It was found that the average classification accuracy with Gabor and CCV features for Artificial Neural Network is 91.74% and 84.18%, respectively, providing plausible results comparing to some other existing models.

[1]  Yap-Peng Tan,et al.  Facial part displacement effect on template-based gender and ethnicity classification , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[2]  Ashok Kumar Dwivedi.  Artificial neural network model for effective cancer classification using microarray gene expression data , 2018, Neural Computing and Applications.

[3]  Jianping Gou,et al.  A generalized mean distance-based k-nearest neighbor classifier , 2019, Expert Syst. Appl..

[4]  Kurt Gray,et al.  The MR2: A multi-racial, mega-resolution database of facial stimuli , 2016, Behavior research methods.

[5]  Nevcihan Duru,et al.  Authentication Technologies for Cloud Technology, IoT, and Big Data , 2019 .

[6]  Khalid Farhan,et al.  Epilepsy Detection from EEG Signals Using Artificial Neural Network , 2018, ICO.

[7]  Ferda Ernawan,et al.  A block-based RDWT-SVD image watermarking method using human visual system characteristics , 2018, Vis. Comput..

[8]  Tal Hassner,et al.  Age and gender classification using convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[9]  Alexandre Bernardino,et al.  Gabor Parameter Selection for Local Feature Detection , 2005, IbPRIA.

[10]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[11]  S. Md. Mansoor Roomi,et al.  Race Classification Based on Facial Features , 2011, 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics.

[12]  J.J. Hopfield,et al.  Artificial neural networks , 1988, IEEE Circuits and Devices Magazine.

[13]  Moacir Ponti,et al.  Image quantization as a dimensionality reduction procedure in color and texture feature extraction , 2016, Neurocomputing.

[14]  Masato Kawade,et al.  Ethnicity estimation with facial images , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[15]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 2002, IEEE Trans. Image Process..

[16]  Guodong Guo,et al.  Support vector machines for face recognition , 2001, Image Vis. Comput..

[17]  Bhavani M. Thuraisingham,et al.  Face Recognition Using Multiple Classifiers , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).

[18]  Thai Hoang Le,et al.  Applying Artificial Neural Networks for Face Recognition , 2011, Adv. Artif. Neural Syst..

[19]  Muhammad Sharif,et al.  Single Image Face Recognition Using Laplacian of Gaussian and Discrete Cosine Transforms , 2012, Int. Arab J. Inf. Technol..

[20]  Khairul Shafee Kalid,et al.  MULTI-FACTOR ATTENDANCE AUTHENTICATION SYSTEM , 2018 .

[21]  Muhammad Nomani Kabir,et al.  Optimal search algorithm in a big database using interpolation–extrapolation method , 2019 .

[22]  M. Narasimha Murty,et al.  Nearest Neighbour Based Classifiers , 2011 .

[23]  M. Kabir,et al.  Numerical tools for some identification problems in industrial applications , 2007 .

[24]  Damon L. Woodard,et al.  Soft biometric classification using periocular region features , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).