Biometric smart lock using convolutional neural network and k-nearest neighbour

Mohd Jasni, Amirul Ian Rashidee (2023) Biometric smart lock using convolutional neural network and k-nearest neighbour. Project Report. Melaka, Malaysia, Universiti Teknikal Malaysia Melaka. (Submitted)

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Abstract

Biometric Smart Lock using Convolutional Neural Network and K-Nearest Neighbors is a sophisticated security system that provides a secure and efficient alternative to traditional lock systems. The project uses the OpenCV library in Python, which provides robust image and video processing capabilities. The system consists of a camera and a fingerprint scanner, which capture the biometric information of the user. The camera captures the face of the user in real-time, which is processed using the Convolutional Neural Network classifier algorithm to detect and identify facial features. Once the face is recognized, the fingerprint scanner is activated to capture the user's fingerprint. The fingerprint data is then compared with a database of known fingerprints using the cv2.FlannBaseMatcher K-NN algorithm to find the most similar fingerprint. If the fingerprint matches with the database, the lock is unlocked, and the user gains access. The use of biometric information ensures that only authorized users are granted access, eliminating the need for traditional keys and passwords. The system's facial and fingerprint recognition capabilities provide an additional layer of security. The Convolutional Neural Network classifier algorithm is particularly wellsuited for facial recognition, while the K-Nearest Neighbors algorithm is ideal for fingerprint matching. The project's focus on security and efficiency makes it an ideal solution for a wide range of applications, including home security, office security, and beyond. Overall, the Biometric Smart Lock using Convolutional Neural Network and K-Nearest Neighbors project provides an innovative and secure approach to lock systems that leverages the power of facial and fingerprint recognition technologies. The project's use of state-of-the-art algorithms and image processing techniques ensures fast and accurate recognition of the user's face and fingerprint.

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