Autofocus Using Out-of-Focus Face Detection Classifiers
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Solution Overview
Problem
Conventional auto focus mechanisms in digital cameras, such as contrast detect and phase detect, are either slow or prone to focus hunting, and face detection methods often fail to detect blurry or out-of-focus faces, leading to unsatisfactory image capture processes.
Innovation Solution
A method utilizing trained classifiers for out-of-focus face detection, determining face size to calculate focus depth, and adjusting lens positions for precise focusing, combined with MEMS technology for fast and reliable tracking, reduces focus hunting and enhances image capture speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional contrast detect auto focus is used, then focusing precision is improved, but image capture speed deteriorates due to slow scanning
Solution Approach 1:
The system performs preliminary face detection and classification before focusing. By detecting faces and determining their out-of-focus state in advance, the system can predict the required focus adjustment direction and magnitude, eliminating the need for slow scanning and enabling direct focus positioning.
Solution Approach 2:
The patent replaces the mechanical scanning process of conventional contrast detect autofocus with a computational approach using machine learning classifiers. Instead of incrementally shifting the lens and measuring contrast at each step, the system uses image processing and classification algorithms to determine focus requirements, significantly speeding up the process while maintaining precision.
2Reliability
If conventional face detection is used, then in-focus face detection is improved, but out-of-focus face detection deteriorates leading to non-detection events
Solution Approach 1:
The face detection process is segmented into multiple stages with different classifiers. The system first applies a coarse classifier to detect potentially out-of-focus faces, then uses a fine classifier to confirm and refine detection. This segmentation allows the system to handle both in-focus and out-of-focus faces effectively without compromising overall detection accuracy.
Solution Approach 2:
The system changes detection parameters dynamically based on the detected face's focus state. Different classifiers with adjusted parameters are applied depending on whether the face appears in-focus or out-of-focus. This adaptive parameter adjustment enables reliable detection across varying focus conditions while maintaining high accuracy for each state.
3Reliability
If conventional auto focus mechanisms are used, then focus acquisition is achieved, but focus hunting occurs when subjects move
Solution Approach 1:
The system continuously monitors face detection results and focus state, providing real-time feedback to adjust focus positioning. By detecting faces and their focus status in each frame and comparing with previous states, the system can predict subject movement and proactively adjust focus, preventing focus hunting rather than reacting to it after it occurs.
Solution Approach 2:
The focus system transitions from static, periodic focus adjustments to a dynamic, continuous adaptation process. The system continuously tracks face positions and focus states, enabling real-time focus adjustments that follow moving subjects smoothly without the oscillatory behavior characteristic of focus hunting in conventional systems.
Data Source
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AI summary
An autofocus method includes acquiring an image of a scene that includes one or more out of focus faces and/or partial faces. The method includes detecting one or more of the out of focus faces and/or partial faces within the digital image by applying one or more sets of classifiers trained on faces that are out of focus. One or more sizes of the one of more respective out of focus faces and/or partial faces is/are determined within the digital image. One or more respective depths is/are determined to the one or more out of focus faces and/or partial faces based on the one or more sizes of the one of more faces and/or partial faces within the digital image. One or more respective focus positions of the lens is/are adjusted to focus approximately at the determined one or more respective depths.