Overview / Usage

The use of gesture as a natural interface serves as a motivating force for research studies in modeling, analyzing, and recognition of gestures. In particular, human-computer intelligent interaction needs vision-based gesture recognition, which involves many interdisciplinary studies. A survey on recent vision-based gesture recognition approaches is given in this paper. We shall review methods of static hand posture and temporal gesture recognition. Several application systems of gesture recognition are also described in this paper. We conclude with some thoughts about future research directions.

Canva 2.0 is a graphic designing tool that is simple to use and wonderful for beginners and pros alike. Canva 2.0 is highly recommended for organizations and companies that want to try new and unique ways of designing library materials. Librarians will enjoy using this program and expressing themselves as they create library brands and accompanying materials. Drawing with the help of your fingertip is something that everyone wanted to do at some point in their life so this makes it highly appreciable for users.

Canva 2.0 music is another important tool that can be used using Artificial Intelligence to detect facial expressions. Detecting facial features is a prerequisite to facial emotion recognition. One of the applications of this input can be for extracting the information to deduce the mood of an individual.

This data can then be used to get a list of songs that comply with the “mood" derived from the input provided earlier. This eliminates the time-consuming and tedious task of manually segregating or grouping songs into different lists and helps in generating an appropriate playlist based on an individual's emotional features. Facial Expression Based Music System parameters are provided. Thus our proposed system focuses on detecting human emotions for developing emotion-based music players, which are the approaches used by available music players to detect emotions, which approach our music player follows to detect human emotions, and how it is better to use our system for emotion detection. A brief idea about our systems working playlist generation and emotion classification is given This is achieved by observing the parts of the face like eves lOS movement etc. These are then classified and compared to trained sets of data. In this research, a human facial expression recognition system will be modeled using the eigenface approach. The proposed method will use the HAAR Cascade classifier to detect the face in an image. Fisher Faces calculation can be utilized for decreasing the high dimensionality of the eigenspace and after that anticipating the test picture upon the eigenface and computing the Euclidean separation between the test picture and meaning of the Eugene faces. The Grayscale image of the face is used by the system to classify basic emotions such as surprise, disgust, sadness, fear, neutral, anger, and happiness. Another important tool is Virtual Keyboard. It helps the designer write the text wherever he wants with a click of an option. Virtual Keyboard comes with 4 different virtual keyboards and supports all languages and keyboard layouts installed in the system.

Methodology / Approach

The research methodology is the description, explanation, and justification of various methods of research for effectively conducting this research. It can be understood as a science of conducting research scientifically. It studies various steps that are generally adopted by researchers while dealing with the research problems along with the logic behind them.

In research, we learned that many designers or young artists who want to shift their pen and paper art to digital art find it too expensive because of quality issues of low budget tabs and price issues with the iPad. Many of them belonging to small villages don't get a proper chance to outcast their skills. So for being a ray of hope for all of them we came up with the idea of Canva 2.0.

4.2 Functional modules design and analysis

The module is based on hand detection and Face recognition for drawing and listening to music respectively

1. **Media pipe **- MediaPipe is a cross-platform framework for building multimodal applied machine learning pipelines. Media-Pipe is a framework for building multimodal (eg. video, audio, any time series data), cross-platform (i.e Android, iOS, web, edge devices) applied ML pipelines.
2. **OpenCV **- (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products.

This module will be mainly responsible for hand detection. We will be using a media pipe for recognition of hand and marking coordinates on hand. We will be assigned to different tasks such as color choosing, saving art, opening the keyboard, closing the keyboard, typing, shifting music. cv2 for creating the frontend some functions like rectangle, circle for creating better UI.

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