联营
判别式
人工智能
计算机科学
卷积神经网络
特征(语言学)
模式识别(心理学)
深度学习
特征工程
机器学习
语言学
哲学
作者
Azher Uddin,Joolekha Bibi Joolee,Young Koo Lee
出处
期刊:IEEE Transactions on Affective Computing
[Institute of Electrical and Electronics Engineers]
日期:2022-04-01
卷期号:13 (2): 864-870
被引量:17
标识
DOI:10.1109/taffc.2020.2970418
摘要
Depression is a serious psychiatric disorder that restricts an individuals ability to work properly in both their daily and professional lives. Usually, the diagnosis of depression often needs a thorough assessment by an expert. Recently, significant consideration has been given to automatic depression prediction for more reliable and efficient depression investigation. In this article, we propose a novel framework to estimate the depression level from video data by employing a two-stream deep spatiotemporal network. Our approach extracts spatial information using the Inception-ResNet-v2 network. In contrast, we introduce a volume local directional number (VLDN) based dynamic feature descriptor to capture facial motions. Then, the feature map obtained from the VLDN is fed into a convolutional neural network (CNN) to obtain more discriminative features. Additionally, we designed a multilayer bidirectional long short-term memory (Bi-LSTM) model to obtain temporal information by integrating the temporal median pooling (TMP) approach into the model. The TMP approach is employed on the temporal fragments of spatial and temporal features. Finally, extensive experimental analysis of two challenging datasets, AVEC2013 and AVEC2014, demonstrates that the proposed approach shows promising performance compared to the existing approaches for depression level prediction.
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