Face Recognition Representative Image Selection Using Weighted Quality Scoring
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Solution Overview
Problem
Existing photo management applications face challenges in automatically selecting a representative image from a group of images using face recognition, often choosing suboptimal photos that do not consider visual or aesthetic qualities, leading to poor representation.
Innovation Solution
A computer-implemented technique that extracts face data from each image, determines a score based on quality parameters such as exposure, resolution, and facial expressions, and selects the image with the highest score as the representative, allowing for weighted importance of each parameter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automatic selection algorithm is used to choose representative photo, then user operation time is reduced, but selection quality deteriorates due to simple algorithms ignoring visual and aesthetic qualities
Solution Approach 1:
The patent transforms the representative photo selection from a simple chronological or random choice into a multi-parameter evaluation system. It introduces quality parameters including exposure assessment, color/gray scale profile analysis, facial expression evaluation, and other aesthetic criteria. By changing the selection parameters from basic metadata to comprehensive visual quality metrics, the system achieves both automation and high selection quality simultaneously.
Solution Approach 2:
The patent replaces manual user selection (mechanical interaction) with an automated computer-based evaluation system. The system uses image processing algorithms to objectively assess photo quality across multiple dimensions, substituting human visual inspection and decision-making with automated computational analysis. This maintains selection quality while eliminating time loss associated with manual evaluation.
2Device complexity
If simple automatic selection algorithm is used (oldest, newest, or most recently added photo), then device complexity is reduced, but selection quality deteriorates by ignoring visual and aesthetic qualities
Solution Approach 1:
The patent segments the photo evaluation process into multiple independent quality assessment modules: exposure assessment module, color/gray scale profile module, facial expression module, and other aesthetic criterion modules. Each module independently evaluates a specific aspect of photo quality, then their results are combined to form an overall quality score. This segmentation allows complex evaluation to be achieved through coordinated simple modules, managing complexity while maintaining high selection quality.
3Measurement precision
If manual selection of representative photo is performed, then selection quality is improved by choosing best combination of attributes, but user operation time increases
Solution Approach 1:
The patent enables the photo selection system to evaluate and select the representative photo autonomously without requiring user intervention. The automated system performs comprehensive quality assessment across multiple parameters, compares all photos in the group, and independently determines the best representative photo. This self-service capability achieves manual-level selection quality while eliminating the time users would spend on manual evaluation and selection.
Data Source
AI summary
A technique for selecting a representative image from a group of digital images includes extracting data representing an image of a face of a person from each image in the group using a face recognition algorithm, determining a score for each image based on one or more quality parameters that are satisfied for the respective image, and selecting the image having the highest score as the representative image for the group. The quality parameters may be based on any quantifiable characteristics of the data. Each of these quality parameters may be uniquely weighted, so as to define the relative importance of one parameter with respect to another. The score for determining the representative image of the group may be obtained by adding together the weights corresponding to each quality parameter that is satisfied for a given image. Once selected, the representative image may be displayed in a graphical user interface.