Automated Facial Feature Recognition and Correction System
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Solution Overview
Problem
Existing digital image processing technologies lack efficient automated methods for recognizing and correcting facial features in digital images, leading to unnatural-looking edited portraits and difficulties in access protection systems.
Innovation Solution
A method and system for automatically identifying coordinates of facial features in digital images, involving face detection, pupil center identification, image rotation and scaling, and the use of a grid-based system with feature vectors and descriptors to accurately locate and correct facial features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual face correction is performed using digital photo editors, then facial features can be corrected, but the corrected image looks artificial and unnatural
Solution Approach 1:
The patent replaces manual mechanical correction operations with an automated computer vision system that detects facial features, generates correction masks, and applies corrections algorithmically. This substitution eliminates the artificial appearance caused by manual editing while maintaining correction effectiveness.
Solution Approach 2:
The system enables the image to correct itself by automatically detecting facial features, generating appropriate correction masks, and applying corrections without human intervention. The correction process is self-directed based on algorithmic analysis of facial geometry and feature relationships.
2Productivity
If automated face recognition is implemented, then correction efficiency is improved, but accurate identification of facial features is required which increases system complexity
Solution Approach 1:
The patent segments the face correction problem into distinct components: facial feature detection, mask generation, and correction application. Each component is handled by specialized algorithms working in sequence, reducing overall system complexity while maintaining high automation efficiency.
Solution Approach 2:
The system introduces intermediate processing stages including feature detection algorithms and mask generation as mediators between raw input images and final corrected outputs. These intermediaries simplify the overall transformation by breaking it into manageable computational steps.
3Reliability
If manual face comparison is performed for access protection, then identification can be made, but the process is time-consuming and requires comparison of multiple pictures
Solution Approach 1:
The system performs preliminary automated extraction and storage of facial feature coordinates and characteristics during image capture or preprocessing. When access protection is needed, these pre-extracted features are quickly compared against stored profiles, eliminating the need for manual comparison of multiple pictures and significantly reducing identification time.
Data Source
AI summary
A system, method and computer program product for automatically identifying coordinates of facial features in digital images. The facial images are detected and pupil coordinates are calculated. First, face identification method is applied. Then, the centers of the pupils are identified. The image is rotated, scaled and the portion of the image is cut out so that the pupils are located on the horizontal line and are located at fixed coordinates. Subsequently an original image with facial features can be identified.