Stacked deep convolutional auto-encoders for emotion recognition from facial expressions

Ariel Ruiz-Garcia, Mark Elshaw, Abdulrahman Altahhan, Vasile Palade

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

32 Citations (Scopus)


Emotion recognition is critical for everyday living and is essential for meaningful interaction. If we are to progress towards human and machine interaction that is engaging the human user, the machine should be able to recognize the emotional state of the user. Deep Convolutional Neural Networks (CNN) have proven to be efficient in emotion recognition problems. The good degree of performance achieved by these classifiers can be attributed to their ability to self-learn a down-sampled feature vector that retains spatial information through filter kernels in convolutional layers. Given the view that random initialization of weights can lead to convergence to non-optimal local minima, in this paper we explore the impact of training the initial weights in an unsupervised manner. We study the effect of pre-training a Deep CNN as a Stacked Convolutional Auto-Encoder (SCAE) in a greedy layer-wise unsupervised fashion for emotion recognition using facial expression images. When trained with randomly initialized weights, our CNN emotion recognition model achieves a performance rate of 91.16% on the Karolinska Directed Emotional Faces (KDEF) dataset. In contrast, when each layer of the model, including the hidden layer, is pre-trained as an Auto-Encoder, the performance increases to 92.52%. Pre-training our CNN as a SCAE also reduces training time marginally. The emotion recognition model developed in this work will form the basis of a real-time empathic robot system.

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781509061815, 9781509061822
ISBN (Print) 9781509061839
Publication statusPublished - 3 Jul 2017
Event2017 International Joint Conference on Neural Networks - Anchorage, United States
Duration: 14 May 201719 May 2017


Conference2017 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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