Gesture Detection System

MediaPipe Gesture Detection is an ultrafast face detection solution that comes with 6 landmarks and multi-face support. It is based on BlazeFace, a lightweight and well-performing face detector tailored for mobile GPU inference. The detector?s super-realtime performance enables it to be applied to a

Project Title

Gesture Detection System

Project Area of Specialization

Computer Science

Project Summary

MediaPipe Gesture Detection is an ultrafast face detection solution that comes with 6 landmarks and multi-face support. It is based on BlazeFace, a lightweight and well-performing face detector tailored for mobile GPU inference. The detector’s super-realtime performance enables it to be applied to any live viewfinder experience that requires an accurate facial region of interest as an input for other task-specific models, such as 3D facial keypoint or geometry estimation (e.g., MediaPipe Face Mesh), facial features or expression classification, and face region segmentation. BlazeFace uses a lightweight feature extraction network inspired by, but distinct from MobileNetV1/V2, a GPU-friendly anchor scheme modified from Single Shot MultiBox Detector (SSD), and an improved tie resolution strategy alternative to non-maximum suppression.

Project Objectives

Purpose

Naming style may differ slightly across platforms/languages.

DETECTIONS

Collection of detected faces, where each face is represented as a detection proto message that contains a bounding box and 6 key points (right eye, left eye, nose tip, mouth center, right ear tragion, and left ear tragion). The bounding box is composed of xmin and width (both normalized to [0.0, 1.0] by the image width) and ymin and height (both normalized to [0.0, 1.0] by the image height). Each key point is composed of x and y, which are normalized to [0.0, 1.0] by the image width and height respectively.

Project Implementation Method

Overall Task Throughout Semester

• Selection of FYP Project
• Working on Project 
• Problem Statement
• Cost benefit analysis of program
• Use case Diagrams
• Sequence Diagrams

Benefits of the Project

Benefits of gesture recognition include improved safety — since drivers do not have to take their attention off the road as much as they would with touch controls — and the simple convenience of being able to control vehicle functions with deliberate gestures rather than a potentially complex menu scheme.

As an agile tool, FRS will benefit different users differently. Governments around the world have begun experimenting with FRS in law enforcement, military, and intelligence operations. Additionally, FRS has the potential to benefit governments in other functions, such as the provision of humanitarian services. Corporations will realize benefits from FRS in innumerable ways over time, but some immediate examples exist in security, marketing, banking, retail, and health care.

Technical Details of Final Deliverable

Gesture detection involves separating image windows into two classes; one containing faces (tarning the background (clutter). It is difficult because although commonalities exist between faces, they can vary considerably in terms of age, skin colour and facial expression. The problem is further complicated by differing lighting conditions, image qualities and geometries, as well as the possibility of partial occlusion and disguise. An ideal face detector would therefore be able to detect the presence of any face under any set of lighting conditions, upon any background. The face detection task can be broken down into two steps. The first step is a classification task that takes some arbitrary image as input and outputs a binary value of yes or no, indicating whether there are any faces present in the image. The second step is the face localization task that aims to take an image as input and output the location of any face or faces within that image as some bounding box with (x, y, width, height). The face detection system can be divided into the following steps:-

1. Pre-Processing:

To reduce the variability in the faces, the images are processed before they are fed into the network. All positive examples that is the face images are obtained by cropping images with frontal faces to include only the front view. All the cropped images are then corrected for lighting through standard algorithms.

1. Classification:

Neural networks are implemented to classify the images as faces or nonfaces by training on these examples. We use both our implementation of the neural network and the Matlab neural network toolbox for this task. Different network configurations are experimented with to optimize the results.

1. Localization:

The trained neural network is then used to search for faces in an image and if present localize them in a bounding box. Various Feature of Face on which the work has done on:- Position Scale Orientation Illumination.

Final Deliverable of the Project

Software System

Core Industry

IT

Other Industries

Education

Core Technology

Artificial Intelligence(AI)

Other Technologies

Internet of Things (IoT)

Sustainable Development Goals

Quality Education

Required Resources

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