Biometric Face Recognition Based on Enhanced Histogram Approach

Biometric face recognition including digital processing and analyzing a subject's facial structure. This system has a certain number of points and measures, including the distances between the main features such as eyes, nose and mouth, angles of features such as the jaw and forehead with the lengths of the different parts of the face. With this information, the implemented algorithm creates a unique model with all the digital data. This model can then be compared with the huge databases of images of the face to identify the subject. The recognition features are retrieved here using histogram equalization technique. A high-resolution result is obtained applying this algorithm under the conditions of a specific image database.

[1]  Hervé Abdi,et al.  Processing Faces and Facial Expressions , 2003, Neuropsychology Review.

[2]  A. Chaudhuri,et al.  The Many Faces of a Neutral Face: Head Tilt and Perception of Dominance and Emotion , 2003 .

[3]  Bin Luo,et al.  Probabilistic two-dimensional principal component analysis and its mixture model for face recognition , 2008, Neural Computing and Applications.

[4]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[5]  Mislav Grgic,et al.  Face recognition in JPEG and JPEG2000 compressed domain , 2009, Image Vis. Comput..

[6]  Marcos Faúndez-Zanuy,et al.  Biometric Recognition Performing in a Bioinspired System , 2009, Cognitive Computation.

[7]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[8]  T. Valentine,et al.  Face recognition and emotional valence: processing without awareness by neurologically intact participants does not simulate covert recognition in prosopagnosia , 2001, Cognitive, affective & behavioral neuroscience.

[9]  Jean-Paul Gauthier,et al.  Generalized Fourier Descriptors with Applications to Objects Recognition in SVM Context , 2007, Journal of Mathematical Imaging and Vision.

[10]  Michael Murias,et al.  The Role of Face Familiarity in Eye Tracking of Faces by Individuals with Autism Spectrum Disorders , 2008, Journal of autism and developmental disorders.

[11]  Thomas F Gross,et al.  The Perception of Four Basic Emotions in Human and Nonhuman Faces by Children With Autism and Other Developmental Disabilities , 2004, Journal of abnormal child psychology.

[12]  Yuanyan Tang,et al.  Status of pattern recognition with wavelet analysis , 2008, Frontiers of Computer Science in China.

[13]  Nur Izura Udzir,et al.  State of the Art in Biometric Key Binding and Key Generation Schemes , 2017, Int. J. Commun. Networks Inf. Secur..

[14]  L. Gool,et al.  Configural and featural processing during face perception: A new stimulus set , 2009, Behavior research methods.

[15]  Rabia Jafri,et al.  A Survey of Face Recognition Techniques , 2009, J. Inf. Process. Syst..

[16]  Muzhir Shaban Al Ani.  Face Recognition Approach Based on Wavelet - Curvelet Technique , 2012 .

[17]  Aqeel-ur Rehman,et al.  Security and Privacy Issues in IoT , 2016, Int. J. Commun. Networks Inf. Secur..

[18]  Na Liu,et al.  Multimodal biometrics recognition based on local fusion visual features and variational Bayesian extreme learning machine , 2016, Expert Syst. Appl..

[19]  C. Deruelle,et al.  Motion and Emotion: A Novel Approach to the Study of Face Processing by Young Autistic Children , 2001, Journal of autism and developmental disorders.

[20]  Sridha Sridharan,et al.  Super-resolution for biometrics: A comprehensive survey , 2018, Pattern Recognit..

[21]  Marcos Faúndez-Zanuy,et al.  Biometric Face Recognition with Different Training and Testing Databases , 2007, COST 2102 Workshop.

[22]  Muzhir Shaban Al-Ani,et al.  An Efficient Approach for Human Face Recognition , 2015 .