Face Recognition Using the SR-CNN Model

In order to solve the problem of face recognition in complex environments being vulnerable to illumination change, object rotation, occlusion, and so on, which leads to the imprecision of target position, a face recognition algorithm with multi-feature fusion is proposed. This study presents a new robust face-matching method named SR-CNN, combining the rotation-invariant texture feature (RITF) vector, the scale-invariant feature transform (SIFT) vector, and the convolution neural network (CNN). Furthermore, a graphics processing unit (GPU) is used to parallelize the model for an optimal computational performance. The Labeled Faces in the Wild (LFW) database and self-collection face database were selected for experiments. It turns out that the true positive rate is improved by 10.97–13.24% and the acceleration ratio (the ratio between central processing unit (CPU) operation time and GPU time) is 5–6 times for the LFW face database. For the self-collection, the true positive rate increased by 12.65–15.31%, and the acceleration ratio improved by a factor of 6–7.

[1]  Huaizu Jiang,et al.  Face Detection with the Faster R-CNN , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[2]  Dongdong Yu,et al.  Fast Rotation-Free Feature-Based Image Registration Using Improved N-SIFT and GMM-Based Parallel Optimization , 2016, IEEE Transactions on Biomedical Engineering.

[3]  Svetlana Lazebnik,et al.  Multi-scale Orderless Pooling of Deep Convolutional Activation Features , 2014, ECCV.

[4]  Ladislav Lenc,et al.  Automatic face recognition system based on the SIFT features , 2015, Comput. Electr. Eng..

[5]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Frédo Durand,et al.  Learning to predict where humans look , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[7]  Yi Yang,et al.  DenseBox: Unifying Landmark Localization with End to End Object Detection , 2015, ArXiv.

[8]  Jun Luo,et al.  Person-Specific SIFT Features for Face Recognition , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[9]  Apurba Das,et al.  Determination of Feature Hierarchy from Gabor and SIFT Features for Face Recognition , 2011, 2011 Second International Conference on Emerging Applications of Information Technology.

[10]  Shuo Yang,et al.  From Facial Parts Responses to Face Detection: A Deep Learning Approach , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[11]  Qi Tian,et al.  Image Classification and Retrieval are ONE , 2015, ICMR.

[12]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[13]  Steven C. H. Hoi,et al.  Face Detection using Deep Learning: An Improved Faster RCNN Approach , 2017, Neurocomputing.

[14]  Zahra Hossein-Nejad,et al.  Image registration based on SIFT features and adaptive RANSAC transform , 2016, 2016 International Conference on Communication and Signal Processing (ICCSP).

[15]  Mona Omidyeganeh,et al.  Face identification using wavelet transform of SIFT features , 2013, 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

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

[17]  Jiwen Lu,et al.  Cost-Sensitive Local Binary Feature Learning for Facial Age Estimation , 2015, IEEE Transactions on Image Processing.

[18]  Vinay S. Shekhar,et al.  Two Novel Detector-Descriptor Based Approaches for Face Recognition Using SIFT and SURF , 2015 .

[19]  Edmund Y. Lam,et al.  Facial expression recognition using deep neural networks , 2015, 2015 IEEE International Conference on Imaging Systems and Techniques (IST).

[20]  Christof Koch,et al.  Learning a saliency map using fixated locations in natural scenes. , 2011, Journal of vision.

[21]  Federico Lecumberry,et al.  Face matching with an a contrario false detection control , 2016, Neurocomputing.

[22]  Li-Jia Li,et al.  Multi-view Face Detection Using Deep Convolutional Neural Networks , 2015, ICMR.

[23]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Jie Xu,et al.  Occlusion-Robust Face Detection Using Shallow and Deep Proposal Based Faster R-CNN , 2016, CCBR.

[25]  Stefano Tornincasa,et al.  Cleft lip pathology diagnosis and foetal landmark extraction via 3D geometrical analysis , 2017 .

[26]  Jang-Hee Yoo,et al.  Real-Time Face Recognition with SIFT-Based Local Feature Points for Mobile Devices , 2013, 2013 1st International Conference on Artificial Intelligence, Modelling and Simulation.

[27]  Tong Zhang,et al.  A Deep Neural Network-Driven Feature Learning Method for Multi-view Facial Expression Recognition , 2016, IEEE Transactions on Multimedia.

[28]  Gautam Narang,et al.  Robust face recognition method based on SIFT features using Levenberg-Marquardt Backpropagation neural networks , 2013, 2013 6th International Congress on Image and Signal Processing (CISP).

[29]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[30]  Jie Lin,et al.  A practical guide to CNNs and Fisher Vectors for image instance retrieval , 2015, Signal Process..

[31]  Jing Li,et al.  Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion , 2018, Sensors.

[32]  Federica Marcolin,et al.  Geometrical descriptors for human face morphological analysis and recognition , 2012, Robotics Auton. Syst..

[33]  Sang Uk Lee,et al.  Rotation invariant face detection using a model-based clustering algorithm , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[34]  M. Sharif,et al.  Hexagonal scale invariant feature transform (H-SIFT) for facial feature extraction , 2015 .

[35]  Thomas Brox,et al.  Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Ayman Alfalou,et al.  Pose invariant face recognition: 3D model from single photo , 2017 .

[38]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[39]  Siddhant Jain,et al.  Fuzzy match index for scale-invariant feature transform (SIFT) features with application to face recognition with weak supervision , 2015, IET Image Process..

[40]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[41]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[42]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[43]  S. Rigatti.  Random Forest. , 2017, Journal of insurance medicine.

[44]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[45]  Kai Xie,et al.  Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning , 2018, Sensors.

[46]  Erik Cambria,et al.  Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[47]  Ying Wu,et al.  Spatially-Constrained Similarity Measurefor Large-Scale Object Retrieval , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[48]  K. N. Balasubramanya Murthy,et al.  Face Recognition using Filtered Eoh-sift☆ , 2016 .

[49]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[50]  Kirmene Marzouki,et al.  Multi-index structure based on SIFT and color features for large scale image retrieval , 2017, Multimedia Tools and Applications.

[51]  B. B. Meshram,et al.  Super-fast parallel eigenface implementation on GPU for face recognition , 2014, 2014 International Conference on Parallel, Distributed and Grid Computing.

[52]  Cong Geng,et al.  Face recognition using sift features , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[53]  Xiaolin Hu,et al.  Joint Training of Cascaded CNN for Face Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[54]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[55]  Claudio A. Perez,et al.  Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count , 2011, Pattern Recognit..

[56]  Qi Tian,et al.  SIFT Meets CNN: A Decade Survey of Instance Retrieval , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[57]  Hamid R. Rabiee,et al.  A novel rotation/scale invariant template matching algorithm using weighted adaptive lifting scheme transform , 2010, Pattern Recognit..