TY - GEN
T1 - Learning Deep Features for Hierarchical Classification of Mobile Phone Face Datasets in Heterogeneous Environments
AU - Narang, Neeru
AU - Martin, Michael
AU - Metaxas, Dimitris
AU - Bourlai, Thirimachos
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/28
Y1 - 2017/6/28
N2 - In this paper, we propose a convolutional neuralnetwork (CNN) based, scenario-dependent and sensor (mobiledevice) adaptable hierarchical classification framework. Ourproposed framework is designed to automatically categorizeface data captured under various challenging conditions, beforethe FR algorithms (pre-processing, feature extraction andmatching) are used. First, a unique multi-sensor database (usingSamsung S4 Zoom, Nokia 1020, iPhone 5S and Samsung S5phones) is collected containing face images indoors, outdoors,with yaw angle from -90 to +90 and at two different distances,i.e. 1 and 10 meters. To cope with pose variations, face detectionand pose estimation algorithms are used for classifying thefacial images into a frontal or a non-frontal class. Next,our proposed framework is used where tri-level hierarchicalclassification is performed as follows: Level 1, face images areclassified based on phone type; Level 2, face images are furtherclassified into indoor and outdoor images; and finally, Level3 face images are classified into a close (1m) and a far, lowquality, (10m) distance categories respectively. Experimentalresults show that classification accuracy is scenario dependent,reaching from 95 to more than 98% accuracy for level 2and from 90 to more than 99% for level 3 classification. Aset of experiments is performed indicating that, the usage ofdata grouping before the face matching is performed, resultedin a significantly improved rank-1 identification rate whencompared to the original (all vs. all) biometric system.
AB - In this paper, we propose a convolutional neuralnetwork (CNN) based, scenario-dependent and sensor (mobiledevice) adaptable hierarchical classification framework. Ourproposed framework is designed to automatically categorizeface data captured under various challenging conditions, beforethe FR algorithms (pre-processing, feature extraction andmatching) are used. First, a unique multi-sensor database (usingSamsung S4 Zoom, Nokia 1020, iPhone 5S and Samsung S5phones) is collected containing face images indoors, outdoors,with yaw angle from -90 to +90 and at two different distances,i.e. 1 and 10 meters. To cope with pose variations, face detectionand pose estimation algorithms are used for classifying thefacial images into a frontal or a non-frontal class. Next,our proposed framework is used where tri-level hierarchicalclassification is performed as follows: Level 1, face images areclassified based on phone type; Level 2, face images are furtherclassified into indoor and outdoor images; and finally, Level3 face images are classified into a close (1m) and a far, lowquality, (10m) distance categories respectively. Experimentalresults show that classification accuracy is scenario dependent,reaching from 95 to more than 98% accuracy for level 2and from 90 to more than 99% for level 3 classification. Aset of experiments is performed indicating that, the usage ofdata grouping before the face matching is performed, resultedin a significantly improved rank-1 identification rate whencompared to the original (all vs. all) biometric system.
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U2 - 10.1109/FG.2017.139
DO - 10.1109/FG.2017.139
M3 - Conference contribution
AN - SCOPUS:85026325281
T3 - Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heterogeneous Face Recognition, HFR 2017, Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation, DCER and HPE 2017 and 3rd Facial Expression Recognition and Analysis Challenge, FERA 2017
SP - 186
EP - 193
BT - Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heterogeneous Face Recognition, HFR 2017, Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation, DCER and HPE 2017 and 3rd Facial Expression Recognition and Analysis Challenge, FERA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017
Y2 - 30 May 2017 through 3 June 2017
ER -