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FPGA BASED GEOMETRICAL MEASUREMENT OF FACE FEATURES

FPGA BASED GEOMETRICAL MEASUREMENT OF FACE FEATURES 2025

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The thesis titled 'FPGA Based Geometric Measurement of Face Features' by Varun Malhotra explores two algorithms for extracting geometric properties of facial features using FPGA and MatLab. The study focuses on identifying fourteen facial features, including the boundaries of the iris and corners of the eyes and mouth, achieving an 85% detection rate across a selected image set. The algorithms utilize a variance projection function for eye feature extraction and a modified SUSAN edge detector for mouth features, both implemented in VHDL on an Altera DE2 FPGA. The work demonstrates efficient processing speeds, with eye detection at 122.22 MHz and mouth detection at 100.57 MHz, while also addressing challenges such as scale independence and resource utilization.

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What is features face recognition?

Facial recognition is a quick and efficient verification system. It can be faster and more convenient compared to other biometric technologies like fingerprints or retina scans. There are also fewer touchpoints in facial recognition compared to entering passwords or PINs.

What are the features extracted for face detection?

Facial feature extraction is the process of extracting face component features like eyes, nose, mouth, etc from human face image. Facial feature extraction is very much important for the initialization of processing techniques like face tracking, facial expression recognition or face recognition.

What are geometric features of the face?

Some of the geometrical features used in [1], including eyebrow thickness and vertical position at the eye center position, nose position and width, the mouths vertical position and width, height of lips, eleven radii describing the chins shape, width of face at nose height, and its width halfway between the nose and

What is a feature-based approach?

Feature-Based Design is a design approach in computer science that involves using predefined features and operations to define sketched features, where the geometry is created by sweeping a planar cross-section or lofting between two or more planar cross-sections with declarative constraints.

How does feature-based face detection work?

Face detection software typically uses AI and ML algorithms, along with statistical analysis and image processing, to find human faces within larger images and distinguish them from nonface objects, such as landscapes, buildings or other human body parts.

What is feature based face detection?

Feature-based or feature-invariant. These methods use features such as a persons eyes or nose to detect a face, but the process can be negatively affected by noise and light.

What is feature-based recognition?

FBR computes the responses of a set of feature detectors over an image and collects them into a feature vector. Novel images and model images are processed in the same way. An image is classified by finding the nearest model in feature space.