Anticipation Algorithm

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Multitasking while driving has become very habitual in this modern era. But multitasking can be a distraction in tasks that requires full attention such as driving, compromising with it can cause accidents. There are various factors that can cause a distraction like adjusting the radio, eating or drinking or using a cellphone, etc. For facial recognition, we considered taking an innovative multi-agent-based computing paradigm which is proven sufficient and promising to the face recognition systems deployed in such distributive environments. But ”FaceNet: A unified embedding for face recognition and clustering” named Google’s research paper from 2015 brought astonishing results. This paper has inspired the development of most of the models existing today. The reason Face Net is a better option is that it adopts the mapping form of the images and creates embedding instead of using any bottleneck layer for verification recognition. The idea of ”ANTICIPATION ALGORITHM” built upon the eye-gazing algorithm is suitable for this concept. This algorithm keeps track of the human eye to assemble in any of the four possibilities following the eye’s position if a pupil is detected.

We have very advanced cars available today with an incredible aspect called driver attention alert. However, not-so-advanced cars can not afford to use this kind of safety measure to its full potential. This issue can be resolved by using the very common device on an individual’s hands, a smartphone. But developing an application only for smartphones is too much of an effort and it can not give the required output. Instead, we thought of developing a desktop application so that we could use the face-cam of the desktop as a facial recognition device. This application can also be used for taking attendance in online classes. The most common method for taking attendance nowadays is the use of google forms at the end of the class. which is not sufficient as the students are very smart to enter the class with minimum time remaining to get the attendance filled. With the world diverting to the era of artificial intelligence, we feel that this issue also needs some automotive approach. We propose a facial recognition portal that takes attendance at random multiple times. So that students would have to sit through a lecture from the moment it starts to the moment it ends. Now, back to the application for the cars, the present method to capture the driver’s facial movements is infrared cameras, to count the driver’s anticipation. But to use a desktop application we are using a normal laptop camera. Facial recognition is a much-experimented concept over the decades. But there has not been a concept with the desired results except for google’s Face Net concept which we have described earlier. Feel free to check out Face Net paper from the link provided below.

FaceNet is a Face Recognition and Clustering Unified Embedding. We present FaceNet, a method that learns a mapping from face feature vectors to a compact Euclidean space in which distances directly correspond to a measure of face similarity. Moreover, We used Tensorflow.TensorFlow is a machine learning library that is open-source and free to just use. It can be used for a range of activities, but it is specific to deep neural network training and inference. Tensorflow is a dataflow and differentiable programming-related symbolic math library. Additionally, To recognize the person’s faces, We have implemented the OpenCV. OpenCV is a programming library that works on real-time vision systems. It was initially invented by Intel and later supported by Willow Garage and Itseez.

To begin, we had to recognize the picture of a human face. We needed an agent who could assist with facial recognition for this. The new agents for recognizing and calculating facial expressions are prohibitively expensive to use. As a result, we decided to use our own custom-built Independent Agent. The picture is captured by our autonomous investigator using the unit sensor. Our agent can run on any operating system. To use the webcam and take real-time images of the user, we used the Python library. The anticipation algorithm will be applied to each generated data from the real-time recognition. Each frame of optimistic trained data would be counted, resulting in a complete human face prediction.


FaceNet: ”A Unified Embedding for Face Recognition and Clustering” was the study we used for Face Recognition. A picture is transformed into a two-dimensional vector, according to the research. As opposed to the vector data that has been conditioned. FaceNet embedding is the process of mapping an image to a 128-dimensional vector. We used Python libraries to make our project work. One of the main libraries that we used is OpenCV which provides a real-time integrated Computer Vision database, software, and hardware. This project is compatible with all operating systems. We have used the NumPy Python library to do the matrix calculations.

Today, we have really modern cars with an impressive feature called driver focus warning. However, vehicles that aren’t as sophisticated can’t afford to use these kinds of safety features to their maximum extent. This problem can be solved by using a smartphone, which is a very popular gadget in people’s hands. However, making an app only for smartphones will be too time-consuming and ineffective. Instead, we considered creating a desktop program that would enable us to use the computer’s built-in camera to perform facial recognition. This concept can also be used for taking attendance in online classes and checking the concentration of each student in the class. The model is performing well because it detects all the faces that are trained properly and checks the concentration of each user using the anticipation algorithm. The surprising thing about the project is that it also recognizes the user that is shown in the photo and will take it as a real user. We learned many things from this project. The first thing is that we should use facet instead of OpenCV because OpenCV has a very low quality of face detection as compared to FaceNet.


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