Drowsiness Prediction Based on Multiple Aspects Using Image Processing Techniques: A Review
S. T. Shirkande, Rutuja B. Bhosale, Shweta S. More, Suyash S. Awate · International Journal for Research in Applied Science and Engineering Technology · 2023
Abstract: Clinical depression is a type of soft biometric trait that can be used to characterize a person. Because of its importance in a variety of legal situations, this mood illness can be included in forensic psychological evaluations. In recent years, research into the automatic detection of depression based on real image has yielded a variety of algorithmic approaches and auditory indicators. Machine learning algorithms have recently been used successfully in a variety of image-based applications. Automatic depression recognition - the recognition of facial expressions linked with sad behaviour – is one of the most important applications. Modern algorithms for detecting depression usually look at both geographical and temporal data separately. This method restricts the capacity to capture a wide range of face expressions as well as the use of different facial parts. This research introduces a novel machine learning strategy for accurately representing face information associated to depressive behaviours from real-world images. Our suggested architecture outperforms state-of-the-art algorithms in automatic depression recognition, according to results from two benchmark datasets.