Hair Color Classification in Face Recognition using Machine Learning Algorithms
Security through automatic human identification is critically important today, and this is largely due to the high volume of communications. Most methods used to identify individuals often use biometrics information, such as facial characteristics. Therefore, face recognition and classification have garnered great interest among computer vision researchers over the past decade. This pattern recognition problem is divided into several subcategories, such as eye or hair detection and classification. Hair is a salient feature in the human face and is one of the most important cues in face detection and recognition. Accurate detection and presentation of the hair region is one of the key components in the automatic synthesis of human facial caricature. In this work, hair color classification through feature extraction and machine learning methods was performed. The impacts of different features and classifiers were investigated using color samples. Support vector machines (SVM) and Kth nearest neighbors (K-NN) were trained by variety sets of statistical and color features, and the trained models were validated. Additionally, the effects of the size of datasets and feature dimensionality reduction were obtained. The best accuracy rate of 99% was achieved through a support vector machine with radial basis kernel function (SVM-RBF) using nine selected statistical and color features.
Kumar, Neeraj, Peter Belhumeur, Shree Nayar, "Facetracer: A search engine for large collections of images with faces," in In European conference on computer vision, pp. 340-353. Springer Berlin Heidelberg, 2008.
Neeraj Kumar, , Alexander Berg, Peter N. Belhumeur, and Shree Nayar, "Describable visual attributes for face verification and image search," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. no. 10, pp. 1962-1977, 2011.
Yacoob, Yaser, and Larry Davis, "Detection, analysis and matching of hair," in In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, vol. 1, pp. 741-748. IEEE, , 2005.
Diedrick Marius, Sumita Pennathur, and Klint Rose, "Face detection using color thresholding and eigenimage template matching," Digital Image Processing project, Stanford University, 2003.
Ion Marques, Supervisor: Manuel Grana., "Algorithms, Face Recognition. "Proyecto Fin de Carrera"," June 16, 2010.
Zheng Ji, Bao-Liang Lu, and Xiao-Chen Lian, "Gender classification by information fusion of hair and face," INTECH Open Access Publisher, 2009.
Steve Lawrence, C. Lee Giles, Ah Chung Tsoi, and Andrew D. Back, "Face recognition: A convolutional neural-network approach," IEEE transactions on neural networks , vol. 8, no. no. 1, pp. 98-113, 1997.
Kurt Hornik, David Meyer, and Alexandros Karatzoglou, "Support vector machines in R," Journal of statistical software , vol. 15, no. no. 9, pp. 1-28, 2006.
Jason Weston, "Support vector machine (and statistical learning theory) tutorial," NEC Labs America, 1998.
Elham Bagherian, Rahmita Wirza OK Rahmat, "Facial feature extraction for face recognition: a review," in In 2008 International Symposium on Information Technology, IEEE, Malaysia, 2008.
Neeraj Kumar, Alexander C. Berg, Peter N. Belhumeur, Shree K. Nayar, "Attribute and simile classifiers for face verification," in In 2009 IEEE 12th International Conference on Computer Vision, pp. 365-372. IEEE, 2009., 2009.
Saman Sarraf, Cristina Saverino, Halleh Ghaderi, John Anderson, "Brain network extraction from probabilistic ICA using functional Magnetic Resonance Images and advanced template matching techniques," in Electrical and Computer Engineering (CCECE), 2014 IEEE 27th Canadian Conference, Toronto, 2014.
Saman Sarraf , Cristina Saverino, Ali Mohammad Golestani, "A Robust and Adaptive Decision-Making Algorithm for Detecting Brain Networks Using Functional MRI within the Spatial and Frequency Domain," in The IEEE International Conference on Biomedical and Health Informatics (BHI) , Las Vegas, 2016.
Saman Sarraf, Jian Sun, "ADVANCES IN FUNCTIONAL BRAIN IMAGING: A COMPREHENSIVE SURVEY FOR ENGINEERS AND PHYSICAL SCIENTISTS.," International Journal of Advanced Research, vol. 4, no. 8, pp. 640-660, 2016.
Saman Sarraf , Ehsan Marzbanrad , Hamid Mobedi, "Mathematical Modeling for Predicting Betamethasone Profile and Burst Release From In Situ Forming Systems Based On PLGA," in Electrical and Computer Engineering (CCECE), 2014 IEEE 27th Canadian Conference on, Toronto, 2014.
- There are currently no refbacks.