Fast large-scale face recognition method and system
A face recognition, large-scale technology, applied in the field of face recognition, can solve the problems of slow recognition speed and unstable query performance, and achieve the effect of large index scale, good scalability and strong stability
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Embodiment 1
[0034] Such as Figure 4 As shown, a fast large-scale face recognition method is disclosed in this implementation, comprising the following steps:
[0035] Collecting a face image to be recognized, and extracting face features from the face image;
[0036] Searching for a sample feature matching the face feature in the face feature cache: if finding a sample feature matching the face feature in the face feature cache, output the identity information of the successfully matched sample feature; If no sample feature matching the face feature is found in the face feature cache, then search for a sample feature matching the face feature in the face feature metric space index library:
[0037] If a sample feature matching the face feature is found in the facial feature metric space index library, the identity information of the successfully matched sample feature is output, and the search time of the face feature is recorded, Judging whether the search time is greater than the pre...
Embodiment 2
[0043] Embodiment 2 is a preferred embodiment of Embodiment 1. It differs from Embodiment 1 in that it introduces the specific structure of the fast large-scale face recognition system, and describes the specific steps of the fast large-scale face recognition method. for refinement:
[0044] Such as figure 1 As shown, in this embodiment, a fast large-scale face recognition system is disclosed, including:
[0045] The video acquisition module collects face video images through the camera;
[0046] Face detection module, which detects faces from the video captured by the camera;
[0047] The feature extraction module is used to extract features from face images, which is realized by a high-precision lightweight neural network model;
[0048] In order to realize the real-time extraction of facial features, the present invention adopts a lightweight deep neural network architecture, which has the remarkable characteristics of high precision and low delay.
[0049] The face fea...
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