Camera Non-Blinking Detection via Pre-Post Image Burst Analysis
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Solution Overview
Problem
Existing camera devices often capture images with subjects having closed eyes or blinking, resulting in unappealing digital images due to poor eye state detection.
Innovation Solution
A camera device equipped with a non-blinking avoidance system that includes modules for face and eye detection, capturing multiple images simultaneously, and selecting the best image based on predefined feature values and image resolution to ensure open eyes are captured.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the camera captures images at normal shutter speed, then the image capture process is simple and fast, but the subject's eyes may be closed or blinking resulting in poor image quality
Solution Approach 1:
The system performs preliminary actions by capturing a burst of pre-images before the main exposure and a burst of post-images after the main exposure. These preliminary images are used to detect eye state and determine whether the main image will have open eyes, allowing the system to prepare and select the best image before final capture completion.
Solution Approach 2:
The system uses feedback by analyzing eye state detection results from pre-images and post-images to determine whether to keep or discard the main captured image. The eye state information feeds back into the image selection process, ensuring that only images with open eyes are selected as final outputs.
2Reliability
If the camera captures multiple images to avoid blinking, then the image quality improves, but the device complexity and processing time increase
Solution Approach 1:
The system segments the image capture process into distinct phases: capturing pre-images before exposure, capturing the main image during exposure, and capturing post-images after exposure. Each segment serves a specific purpose - pre-images and post-images for eye state detection, main image for final output. This segmentation allows complex multi-image capture to be managed through modular, organized stages.
3Speed
If the camera uses fast shutter speed to capture the moment, then the capture process is quick, but it increases the likelihood of capturing closed eyes
Solution Approach 1:
The system applies dynamics by using different shutter speeds at different stages of the capture process. Fast shutter speed is used for capturing pre-images and post-images to freeze motion and accurately detect eye state. The main image uses a different exposure time optimized for final quality. This dynamic adjustment of shutter speed across different capture phases optimizes both detection accuracy and image quality.
Data Source
AI summary
In a method for capturing non-blinking images of people using a camera device, the camera device includes an image capturing unit and a storage system. The image capturing unit captures a series of digital images of a group of persons. A face feature value and an eyes feature value of people are predefined, and are stored in the storage system. The number of faces is detected from each of the digital images according to the face feature value, and the number of eyeballs is detected from each of the face area according to the eyes feature value. The method calculates the ratio of the number of faces and the number of eyeballs, and selects the digital image of which the ratio of the face number and the eyeball number is 1:2 as a non-blinking image of the a group of persons.