One of the key challenges that the computer vision is facing is the age prediction. A well efficient CNN is selected for age prediction by performing various CNN operations by taking the categories as age 40 and above age 40. The selected CNN method obtained a training accuracy of 100% at more than 100 epochs. Hence, 100 epochs is considered for training. At this, the validation accuracy achieved is 84.9%. Three kinds of age phases with an age gap of 20,10 and 5 are used to predict the age. The normal method results in very less accuracy. Hence a hierarchical method is formulated. Under the hierarchical method, CNN is trained to estimate the age gaps in decreasing order. Hence not a single classifier, a group of classifiers are used for testing the image. From traditional method to hierarchical method, the 20 age gap accuracy increased from 27% to above 60%, ten age gap increased from 12% to above 35%, and five age gap increased from 5.5% to above 21%. To improve further, the features of the face parts are derived and combined which improves the efficiency compared to normal method, but not good accuracy as Hierarchical method. The combination of hierarchical method along with the face feature extraction method results in a considerable improvement in accuracy.
Smile is one of the important emotions that is essential in computer vision tasks. The greatly influenced part due to it is the lips. By encountering the changes in lips of smile images with respect to no smile, a smile detecting model can design for the computer vision tasks. In this paper, the approach is to evaluate the spread of lips. The lips movement distance is evaluated with respect to the eyes. 68 landmark points of dlib are used for this purpose. The left and right corners of lips are evaluated with the left and right eyes respectively using the count of landmark points (l and r). The secondary parameters - average, Maximum, and maxavgsum of l and r are used for evaluating the lip expansion variation. For each value of these parameters that can attain from l and r, the count of no smile images below it and count of smile images above it is considered and calculated the attainable efficiency. The value of secondary parameter having the maximum efficiency is defined as the threshold. The maximum efficiency that is attained due to average, Maximum and maxavgsum are 80.06, 67.3 and 78.54 respectively at the thresholds 2, 3 and 4.5 respectively.
Developments in tracking of objects is one of the key breakthroughs in computer vision in recent years. A video is a continuous flow of frames. By analysing the difference between a frame to its successive, one can estimate the movement of object. In this paper, the movement of person is detected with the help of two cameras facing opposite to each other. The detected persons face is recognized with the faces in the database, his data about the movement is updated in the excel sheet from time to time and the same is applied in the real-world problem of vigilance on invigilators in the examination hall. Examinations are inescapable to conclude a course. Devising the students and watchdogs into the examination halls are the part of proper conduction of Examinations. One of the key things in the administration of examinations is the issuance of invigilations. The faculty act as invigilators. It is arduous to mull over his/her designation and experience while earmarking the invigilations manually. This paper presents a cybernetic and contemplative method of dole out using Django and tracking the movements of each invigilator using Opencv.
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