Understanding Users’ Satisfaction towards Public Transit System in India

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Benefits of Face mask detection

IV. RESULT AND ANALYSIS

The model is trained, certified and tested on two databases. According to data 1, the method obtains an accuracy of up to 95.77% indicating how this adjusted accuracy reduces the cost of error. Data set 2 is more flexible than database 1 as it has many faces in the frame and different types of masks of different colors as well. Therefore, the model obtains 94.58% accuracy in the database. 8 shows the difference between training and loss of validation associated with database 2. One of the main reasons for achieving this accuracy is in MaxPooling. It provides consistent translation on internal representation and a reduction in the number of parameters the model should study. This process of sample-based discretization lowers the sample input representations that comprise the image, by reducing its size. The number of neurons has a set value of 64 which is not very high. Too high a number of neurons and filters can lead to worse performance. Improved filter values and the size of the swimming pool help to filter the main part (face) of the image to determine the presence of the mask properly without causing excessive alignment. The system can detect slightly closed face or mask or hair or hand. It looks at the occlusion degree of the four regions - nose, mouth, chin and eye to distinguish between an mask with an annotation or a face-covered face. Therefore, a full face mask covering the nose and chin will be treated only as a “mask” by the model.

epochs vs loss corresponding to dataset 2

epochs vs accuracy corresponding to dataset 2

The main challenges facing the route primarily include various angles and a lack of clarity. Blurred moving faces in video streaming make it very difficult. However, following the trajectories of several video frames helps to make a better decision - "with a mask" or "without a mask".

V. CONCLUSION

In this paper, we briefly describe the motivation for the work at the beginning. Next, we demonstrate the learning function and performance of the model. Using basic ML tools and simplified techniques the method achieves high sensible accuracy. It can be used for a variety of applications. Wearing a mask may be an obligation soon, given the Covid-19 problem. Many government service providers will ask customers to wear a mask properly in order to use their services. The model used will contribute significantly to the public health care system. In the future it can be extended to determine whether a person is well-dressed or not. The model can also be developed to determine whether the mask is viral or not, i.e. the type of mask is surgical, N95 or not.