PT Unknown
AU Rain Eric Haamer
   Kaustubh Kulkarni
   Nasrin Imanpour
   Mohammad Ahsanul Haque
   Egils Avots
   Michelle Breisch
   Kamal Nasrollahi
   Sergio Escalera
   Cagri Ozcinar
   Xavier Baro
   Ahmad R. Naghsh-Nilchi
   Thomas B. Moeslund
   Gholamreza Anbarjafari
TI Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification
BT 8th International Workshop on Human Behavior Understanding
PY 2018
DI 10.1109/FG.2018.00098
AB Facial dynamics can be considered as unique signatures for discrimination between people. These have started to become important topic since many devices have the possibility of unlocking using face recognition or verification. In this work, we evaluate the efficacy of the transition frames of video in emotion as compared to the peak emotion frames for identification. For experiments with transition frames we extract features from each frame of the video from a fine-tuned VGG-Face Convolutional Neural Network (CNN) and geometric features from facial landmark points. To model the temporal context of the transition frames we train a Long-Short Term Memory (LSTM) on the geometric and the CNN features. Furthermore, we employ two fusion strategies: first, an early fusion, in which the geometric and the CNN features are stacked and fed to the LSTM. Second, a late fusion, in which the prediction of the LSTMs, trained independently on the two features, are stacked and used with a Support Vector Machine (SVM). Experimental results show that the late fusion strategy gives the best results and the transition frames give better identification results as compared to the peak emotion frames.
ER