The psychological health of a person plays an important role in their daily life activities. The paper addresses depression issues with the machine learning model using facial expressions of the patient. Some research has already been done on visual based on depression detection methods, but those are illumination variant. The paper uses feature extraction using LBP (Local Binary Pattern) descriptor, which is illumination invariant. The Viola-Jones algorithm is used for face detection and SVM (support vector machine) is considered for classification along with the LBP descriptor to make a complete model for depression level detection. The proposed method captures frontal face from the videos of subjects and their facial features are extracted from each frame. Subsequently, the facial features are analyzed to detect depression levels with the post-processing model. The performance of the proposed system is evaluated using machine learning algorithms in MATLAB. For the real-time system design, it is necessary to test it on the hardware platform. The LBP descriptor has been implemented on FPGA using Xilinx VIVADO 16.4. The results of the proposed method show satisfactory performance and accuracy for depression detection comparison with similar previous work.

一个人的心理健康在他们的日常生活活动中起着重要的作用。该论文通过使用患者面部表情的机器学习模型解决了抑郁症问题。一些基于抑郁检测方法的视觉研究已经完成，但这些都是光照变量。该论文使用 LBP（局部二进制模式）描述符进行特征提取，这是光照不变的。Viola-Jones 算法用于人脸检测，SVM（支持向量机）与 LBP 描述符一起考虑用于分类，以制作完整的抑郁水平检测模型。所提出的方法从主体的视频中捕获正面人脸，并从每一帧中提取他们的面部特征。随后，分析面部特征以使用后处理模型检测抑郁程度。在 MATLAB 中使用机器学习算法评估所提出系统的性能。对于实时系统设计，需要在硬件平台上进行测试。LBP 描述符已使用 Xilinx VIVADO 16.4 在 FPGA 上实现。与以前的类似工作相比，所提出的方法的结果显示出令人满意的抑郁症检测性能和准确性。