A Dataset for Comparing Mirrored and Non-Mirrored Male Bust Images for Facial Recognition

Facial recognition, as well as other types of human recognition, have found uses in identification, security, and learning about behavior, among other uses. Because of the high cost of data collection for training purposes, logistical challenges and other impediments, mirroring images has frequently been used to increase the size of data sets. However, while these larger data sets have shown to be beneficial, their comparative level of benefit to the data collection of similar data has not been assessed. This paper presented a data set collected and prepared for this and related research purposes. The data set included both non-occluded and occluded data for mirroring assessment.

[1]  Luhong Liang,et al.  A detector tree of boosted classifiers for real-time object detection and tracking , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[2]  Stephen Milborrow.  The MUCT Landmarked Face Database , 2010 .

[3]  Zhi-Hua Zhou,et al.  Face recognition from a single image per person: A survey , 2006, Pattern Recognit..

[4]  S B Puri,et al.  3D FACE RECOGNITION UNDER EXPRESSIONS, OCCLUSIONS AND POSE VARIATION , 2018 .

[5]  K. G. Subramanian,et al.  Psychophysically Inspired Bayesian Occlusion Model to Recognize Occluded Faces , 2011, CAIP.

[6]  Timo Ahonen,et al.  Recognition of blurred faces using Local Phase Quantization , 2008, 2008 19th International Conference on Pattern Recognition.

[7]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[8]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[9]  Horst-Michael Groß,et al.  A real-time facial expression recognition system based on Active Appearance Models using gray images and edge images , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[10]  Bogdan Miclut.  Committees of Deep Feedforward Networks Trained with Few Data , 2014, GCPR.

[11]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Lucas D. Introna,et al.  Picturing Algorithmic Surveillance: The Politics of Facial Recognition Systems , 2002, Surveillance & Society.

[13]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[14]  Sun-Yuan Kung,et al.  Face recognition/detection by probabilistic decision-based neural network , 1997, IEEE Trans. Neural Networks.

[15]  Anil K. Jain,et al.  Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Mingjing Li,et al.  Robust multipose face detection in images , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Mohammed Yeasin,et al.  Recognition of facial expressions and measurement of levels of interest from video , 2006, IEEE Transactions on Multimedia.

[18]  Sharath Pankanti,et al.  Biometrics: a tool for information security , 2006, IEEE Transactions on Information Forensics and Security.

[19]  Mohsin Karovaliya,et al.  Enhanced Security for ATM Machine with OTP and Facial Recognition Features , 2015 .

[20]  Kevin W. Bowyer,et al.  Face recognition technology: security versus privacy , 2004, IEEE Technology and Society Magazine.

[21]  William J. Christmas,et al.  When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[22]  Majid Nili Ahmadabadi,et al.  Attention control with reinforcement learning for face recognition under partial occlusion , 2011, Machine Vision and Applications.

[23]  Fred Nicolls,et al.  Locating Facial Features with an Extended Active Shape Model , 2008, ECCV.

[24]  L M Patnaik,et al.  Feature Extraction based Face Recognition , Gender and Age Classification , 2010 .

[25]  Takeo Kanade,et al.  The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[26]  Gang Hua,et al.  Labeled Faces in the Wild: A Survey , 2016 .

[27]  Jeremy Straub,et al.  Human face images from multiple perspectives with lighting from multiple directions with no occlusion, glasses and hat , 2019, Data in brief.

[28]  Vincent Lepetit,et al.  On rendering synthetic images for training an object detector , 2014, Comput. Vis. Image Underst..

[29]  Habib Rostami,et al.  Pgu-Face: A dataset of partially covered facial images , 2016, Data in brief.

[30]  F. Tarres,et al.  A novel method for face recognition under partial occlusion or facial expression variations , 2005, 47th International Symposium ELMAR, 2005..

[31]  Yuxiao Hu,et al.  MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition , 2016, ECCV.

[32]  Jongsun Kim,et al.  Effective representation using ICA for face recognition robust to local distortion and partial occlusion , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Aleix M. Martínez,et al.  Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Shihong Lao,et al.  Security Management for Mobile Devices by Face Recognition , 2006, 7th International Conference on Mobile Data Management (MDM'06).

[35]  Norbert Krüger,et al.  Face Recognition and Gender determination , 1995 .

[36]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.