Classification of Humans into Ayurvedic Prakruti Types using Computer Vision

Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a person’s ‘Prakruti’. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the palm, body frame, strength of digestion and many more as well as the psychological features like their nature (introverted, extroverted, calm, excitable, intense, laidback), and their reaction to stress and diseases. All these features are coded in the constituents at the time of a person’s creation and do not change throughout their lifetime. Ayurvedic doctors analyze the Prakruti of a person either by assessing the physical features manually and/or by examining the nature of their heartbeat (pulse). Based on this analysis, they diagnose, prevent and cure the disease in patients by prescribing precision medicine. This project focuses on identifying Prakruti of a person by analysing his facial features like hair, eyes, nose, lips and skin colour using facial recognition techniques in computer vision. This is the first of its kind research in this problem area that attempts to bring image processing into the domain of Ayurveda

Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and $K$-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan

Personal Authentication System Based Iris Recognition with Digital Signature Technology

Authentication based on biometrics is being used to prevent physical access to high-security institutions. Recently, due to the rapid rise of information system technologies, Biometrics are now being used in applications for accessing databases and commercial workflow systems. These applications need to implement measures to counter security threats.  Many developers are exploring and developing novel authentication techniques to prevent these attacks. However, the most difficult problem is how to keep biometric data while maintaining the practical performance of identity verification systems. This paper presents a biometrics-based personal authentication system in which a smart card, a Public Key Infrastructure (PKI), and iris verification technologies are combined. Raspberry Pi 4 Model B+ is used as the core of hardware components with an IR Camera. Following that idea, we designed an optimal image processing algorithm in OpenCV/ Python, Keras, and sci-kit learn libraries for feature extraction and recognition is chosen for application development in this project. The implemented system gives an accuracy of (97% and 100%) for the left and right (NTU) iris datasets respectively after training. Later, the person verification based on the iris feature is performed to verify the claimed identity and examine the system authentication. The time of key generation, Signature, and Verification is 5.17sec,0.288, and 0.056 respectively for the NTU iris dataset. This work offers the realistic architecture to implement identity-based cryptography with biometrics using the RSA algorithm