%0 Conference Proceedings
%T Changes in Facial Expression as Biometric: A Database and Benchmarks of Identification
%A Rain Eric Haamer
%A Kaustubh Kulkarni
%A Nasrin Imanpour
%A Mohammad Ahsanul Haque
%A Egils Avots
%A Michelle Breisch
%A Kamal Nasrollahi
%A Sergio Escalera
%A Cagri Ozcinar
%A Xavier Baro
%A Ahmad R. Naghsh-Nilchi
%A Thomas B. Moeslund
%A Gholamreza Anbarjafari
%B 8th International Workshop on Human Behavior Understanding
%D 2018
%F Rain Eric Haamer2018
%O HUPBA; no proj
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3118), last updated on Fri, 21 Jan 2022 14:41:22 +0100
%X 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.
%U http://refbase.cvc.uab.es/files/HKI2018.pdf
%U http://dx.doi.org/10.1109/FG.2018.00098