Abstract
In literature, many feature extraction methods have been suggested to compute features from videos in order to diagnose depression. But, using a single feature extraction method does not generate good performance. Many works have combined features extracted using different feature extraction methods and applied feature selection using Evolutionary Algorithms (EA) to improve the depression detection accuracy. However, with high dimensional features, the search space and computational complexity for an EA increases and it converges to a sub-optimal solution. In order to reduce the search space and computational complexity of an EA we suggest a two phased evolutionary approach based on the Quantum Whale Optimization Algorithm (QWOA). In the first phase, QWOA is used to reduce and select the optimum combination of feature extraction methods. In the second phase, features computed using the feature extraction methods selected in the first stage are concatenated, and QWOA is used to select the relevant features. Experiments performed on the DAICWOZ dataset demonstrate that the proposed approach significantly reduces the computational complexity and converges to a score of 0.8726 (0.9353) for the F1 Depressed (F1 non-Depressed) category. The obtained depression detection performance exceeds the state-of-the-art results. The optimum combination of features selected are statistically significant for detecting depression.
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Rathi, S., Kaur, B., Agrawal, R.K. (2023). Bi-stage QWOA-Based Efficient Feature Selection for Enhanced Depression Detection Based on Facial Cues. In: Abraham, A., Hanne, T., Gandhi, N., Manghirmalani Mishra, P., Bajaj, A., Siarry, P. (eds) Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022). SoCPaR 2022. Lecture Notes in Networks and Systems, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-031-27524-1_24
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