Hybrid Face Recognition System Using Haar Cascade and Local Binary Pattern Histogram for Automatic Smart Door Access
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Abstract
Conventional key-based door access systems remain vulnerable to loss, duplication, and misuse, motivating biometric access mechanisms that can operate reliably under constrained resources. This paper proposes a hybrid face recognition system for automatic smart door access that integrates Haar Cascade for real-time face detection and Local Binary Pattern Histogram (LBPH) for lightweight recognition. The system adopts an IoT-assisted architecture in which an ESP32-CAM captures facial frames and transmits them via Wi-Fi to a processing unit that performs detection, recognition, and access-decision logic, subsequently activating a solenoid lock and generating Telegram-based event logs to support traceability. Haar Cascade efficiently localizes facial regions via multi-scale scanning and cascade-based rejection, while LBPH encodes local texture patterns into histograms for identity matching with low computational overhead. The proposed system was evaluated with three enrolled participants across seven trials, covering illumination variation, multiple faces, and a controlled failure case designed to simulate an unauthorized or non-verifiable attempt. Experimental results show a successful unlocking rate of 85.71% (6/7 trials) with an average end-to-end response time of approximately 2.20 s, demonstrating that the system can operate within practical latency constraints for real-time access control. In addition, the controlled failure case was correctly rejected, indicating a conservative security posture that prevents unsafe unlocking when facial evidence is insufficient. Overall, the findings suggest that the proposed classical-method hybrid design provides an effective balance between recognition reliability, latency, and deployment feasibility for IoT-enabled smart door security applications, particularly in cost- and power-constrained environments.
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