This article outlines the mathematical and statistical foundations of digital image processing, explaining how continuous physical light is converted into digital data through sampling and quantization, and how probability theory is used to mitigate noise via specific filters like Mean and Median operations.

It highlights the strategic importance of choosing between the spatial and frequency domains for analysis, noting that operations like removing periodic noise are more efficient in the frequency domain. These concepts are applied to a practical case study of a facial recognition-based attendance system, which integrates traditional pre-processing with modern AI architectures (CNNs) to handle face detection and feature extraction.

The discussion extends to the system's industrial scalability and security, emphasizing a modular architecture that separates image processing from business logic and utilizes a dual-database strategy for managing user records and facial embeddings.

To address security vulnerabilities, the system incorporates "anti-spoofing" technology (Liveness Detection) that leverages Fourier Transform analysis. This mechanism detects the specific frequency signatures associated with printed photos or digital screens, effectively distinguishing between a live human face and a fraudulent replica.

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