Facial image correction method, device and storage medium
A face image and correction method technology, which is applied in image enhancement, image analysis, image data processing, etc., can solve problems such as incapable of camera acquisition and inability to satisfy users, and achieve the effect of accurate facial recognition
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Embodiment 1
[0029] like figure 1 As shown, the first aspect of the present disclosure relates to a facial image correction method, comprising:
[0030] Step 101, the multi-type sample library establishment step is to establish a multi-species sample library, wherein the sample ID of the corresponding species and the facial image of the species are stored in the sample library of each species. For example, the human face sample library LFPW, AFLW, BioID, ICCV13, MVFW, olivettifaces; the cat face sample library can randomly collect a sufficient number (such as 200) of cat faces of various varieties as the cat face sample library; the dog face sample library can Randomly collect a sufficient number (such as 200) of dog faces of each breed as a cat face sample library.
[0031] Step 102, multi-category recognition model training step, using machine learning algorithms to learn samples in multi-type sample databases for multiple facial images of species with the same sample ID, to obtain mult...
Embodiment 2
[0051] In image recognition, the quality of the image directly affects the design of the recognition algorithm and the accuracy of the effect. Therefore, in addition to the optimization of the algorithm, the preprocessing technology occupies a very important factor in the entire project.
[0052] This embodiment takes into account that in the process of real-time image acquisition, there are usually factors such as light and shade, shadows, and complex backgrounds. Therefore, in a preferred embodiment, the obtained image to be recognized can be preprocessed to eliminate irrelevant information in the image. , recover useful real information, enhance the detectability of relevant information and minimize data, thereby improving the reliability of feature extraction, image segmentation, matching and recognition.
[0053] like image 3 As shown, in step 103', the obtained image to be recognized is firstly preprocessed. The preprocessing process generally includes steps such as di...
Embodiment 3
[0057] This embodiment is a preferred embodiment, which specifically adopts the various parameters required for the correction function obtained from the multi-face feature calculation, and then establishes a mapping function through the mapping relationship between each facial feature point of the multi-image, and performs multi-angle facial images. The correction method is used to adjust the error caused by non-frontal image recognition. refer to Figure 5 , step 105 may specifically include:
[0058] Step 1051, the parameter acquisition step, using multiple facial features to calculate the parameters required by the correction function.
[0059] For example, the image depths of the above-mentioned feature points of the mouth, nose tip, eyes, and eyebrows in each facial image are obtained by calculating each facial feature in multiple facial images obtained; and in each facial image, the mouth, nose tip, eyes, The number of pixels of the above-mentioned feature points of t...
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