Design and Implementation of WEB-based Multi-entry Face Recognition Customer Management System
Bibliographic record
Abstract
In recent years, with the rapid development of the automobile sales market, automobile 4S stores, as one of the main channels for automobile sales, are also facing increasing customer management pressure. The 4S car shop customer management systems have shortcomings such as slow synchronization of information, inefficiency and time-consumption, unable to meet the needs of the pre-sale, after-sale and technical support. These problems seriously affect customer satisfaction and loyalty, which in turn affects the sales performance of 4S stores. To these problems, this paper mainly combines multi-face recognition technology and multi-feature cascade database to design and implement a Web-based multi-entry face recognition customer management system for 4S car shop. The system adopts a multi feature cascaded database as the core technology for storing and processing data, which can achieve synchronization of multi entry customer recognition with high recognition accuracy. It effectively solves the problems of traditional customer management systems, improves the efficiency and accuracy of customer management, and has certain practical value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".