Kinship verification using Deep Neural Network Models

The recent advancements in the field of image processing has led to giving the importance of kinship verification. In this paper, we propose methodology based on Deep Neural Networks (DNN) and Support Vector Machine (SVM) classifier for the blood relation identification of human faces in the images. We examined the best parameters for every feature extraction technique such as: Grey-Level Co-Occurrence Matrix (GLCM), Completed Joint Scale Local Binary Pattern (CJLBP), Alexnet, Resnet on KinFace-I And KinFace-II datasets. The method is made up of two basic stages which are; (1) Feature Extraction (2) Classification. In the proposed method we adopted Alexnet for the process of feature extraction, and the classifier used is support vector machine (SVM). The results obtained using proposed approach gives better results in comparison to many other approaches that were used previously.

[1]  Haibin Yan,et al.  Kinship verification from facial images by scalable similarity fusion , 2016, Neurocomputing.

[2]  Min Xu,et al.  Kinship Verification Using Facial Images by Robust Similarity Learning , 2016 .

[3]  Mohammad H. Mahoor,et al.  Going deeper in facial expression recognition using deep neural networks , 2015, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[4]  Esa Rahtu,et al.  BSIF: Binarized statistical image features , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[5]  Guodong Guo,et al.  Ensemble similarity learning for kinship verification from facial images in the wild , 2016, Inf. Fusion.

[6]  Haibin Yan,et al.  Kinship verification using neighborhood repulsed correlation metric learning , 2017, Image Vis. Comput..

[7]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[8]  Ming Shao,et al.  Kinship Verification through Transfer Learning , 2011, IJCAI.

[9]  Xudong Jiang,et al.  Deep Coupled ResNet for Low-Resolution Face Recognition , 2018, IEEE Signal Processing Letters.

[10]  Tsuhan Chen,et al.  Towards computational models of kinship verification , 2010, 2010 IEEE International Conference on Image Processing.

[11]  Salim Ouchtati,et al.  LPQ and LDP Descriptors with ML Representation for Kinship verification , 2018 .

[12]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Abdelmalik Taleb-Ahmed,et al.  A Comparative Study On Textures Descriptors In Facial Gender Classification , 2017 .

[14]  Xiaosheng Wu,et al.  Joint-scale LBP: a new feature descriptor for texture classification , 2017, The Visual Computer.

[15]  Jiwen Lu,et al.  Discriminative Multimetric Learning for Kinship Verification , 2014, IEEE Transactions on Information Forensics and Security.

[16]  Fadi Dornaika,et al.  Pyramid multi-level features for facial demographic estimation , 2017, Expert Syst. Appl..

[17]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[18]  Jinhui Tang,et al.  Deep kinship verification , 2015, 2015 IEEE 17th International Workshop on Multimedia Signal Processing (MMSP).

[19]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[20]  Jiwen Lu,et al.  Prototype-Based Discriminative Feature Learning for Kinship Verification , 2015, IEEE Transactions on Cybernetics.

[21]  Abdenour Hadid,et al.  Facial age estimation and gender classification using multi level local phase quantization , 2015, 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT).

[22]  Ming Shao,et al.  Genealogical face recognition based on UB KinFace database , 2011, CVPR 2011 WORKSHOPS.

[23]  F. Albregtsen.  Statistical Texture Measures Computed from Gray Level Coocurrence Matrices , 2008 .