Visual Depression Diagnosis From Face Based on Various Classification Algorithms

Abstract

Most psychologists believe that facial behavior through depression differs from facial behavior in the absence of depression, so facial behavior can be utilized as a dependable indicator for spotting depression. Visual depression diagnosis system (VDD) establishes dependents on expressions of the face that are expense-effective and movable. At this work, the VDD system is designed according to the Facial Action Coding System (FACS) to extract features of the face. The key concept of the Facial Action Coding System (FACS) to explain the whole face behavior utilizing Action Units (AUs), every AU is linked to the motion of unique or maybe further face muscles. Six AUs have utilized as depression features; those action units are AUs 4, 5, 6, 7, 10, and 12. The datasets that employed to evaluate the performance of the proposed system are gathered for 125 participants (30 males, 95 females); many of them are among 17-60 years of age. At the final step of the current system, four kinds of classification techniques were applied separately; those classifiers algorithms are KNN, SVM, PCA, and LDA. The outcomes of the simulation indicate that the best outcomes are achieved utilizing the KNN and LDA classifiers, where the success rate is 85%. New classification methods in the VDD system are the key contributions of this research, gather real databases that can utilize to compute the performance of every other VDD system based on face emotions, and choose appropriate features of the face.

Real-Time Driver Depression Monitoring for Accident Prevention in Smart Vehicles

“…Table 4 presents a comparative analysis of various studies proposed for depression detection through different methodologies. Nasser et al [39] developed a model for diagnosing depression from facial expressions, employing various learning algorithms and achieving an accuracy of 85% with the K-Nearest Neighbors (KNN) method. In a study, Katrakazas et al [40] explored depression detection using time-series classification, where they reported a high accuracy rate of 93.6% for their proposed method.…”

Fault Detection and Fault Tolerant Control for Anti-lock Braking Systems )ABS) Speed Sensors by Using Neural Networks

“…The algorithms of NN are typically much faster than those of conventional iterative CF techniques. Additionally, the solution does not require an initial guess, and there is an ability to implement the designed network model with special-purpose hardware and, in this way, take advantage of the full ability of NN, including high processing speed [17][18][19][20].…”

“…This suggested approach is implemented using the principle of fast processing Curve Fitting (CF) executed by Neural Networks (NNs). NNs have been employed in various fields due to, in part, a new powerful algorithm development that affects their ability to process information rapidly and leads to faster response [17][18][19][20]. NNs provide a perfect tool with high accuracy and fast response solution for nonlinear CF problems [15].…”

Textual Dataset Classification Using Supervised Machine Learning Techniques

“…Then, the training phase of KNN saves the training set feature vectors with their related class labels. In the testing phase, the algorithm determines the K value, which can be estimated by trial and error and find out the optimal K value like 2,3 or 5, or it can be identified based on the dataset by using the following equation [14,15,16]:…”

“…The algorithm determines the tested feature vector predicted class using the majority vote of the nearest neighbor's class, i.e., get the most frequent class from the nearest neighbors. The most repeated classes will be assigned to the testing feature vector record [14,15,17].…”