Automated Time Recording Using Vision-Based Multi-Employee Recognition
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Solution Overview
Problem
Existing time recording systems are inefficient when multiple employees attempt to clock in or out simultaneously, leading to technical inefficiencies and delays in processing large volumes of employee time records.
Innovation Solution
Implementing machine learning techniques to identify and recognize multiple employees through image capturing devices, using Convolutional Neural Networks (CNNs) to extract facial and hand gestures, and associate them with employee identifiers and time recording codes for simultaneous electronic time recording.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional time clocks are used to register time for employees, then the system is simple to implement, but it can only process one employee at a time causing delays when multiple employees attempt to clock in or out simultaneously
Solution Approach 1:
The patent replaces the mechanical/electronic time clock system with a machine learning-based computer vision system. The system uses deep learning models to detect employees, recognize their identities, and automatically record time actions through image analysis, eliminating the need for physical time clocks and manual punching/swiping operations.
Solution Approach 2:
The system creates digital copies of employees through facial recognition and image capture. Instead of requiring physical interaction with time clocks, the system captures images of employees and uses machine learning models to identify them and record time actions, effectively copying the identification function through digital means.
2Productivity
If machine learning techniques are implemented to simultaneously identify multiple employees, then processing capacity increases, but computational requirements and system complexity increase
Solution Approach 1:
The patent divides the time recording task into separate functional modules: employee detection, facial recognition, time action detection, and time recording. This segmentation allows each component to be optimized independently and processes information in parallel, improving efficiency while managing computational load.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models with employee images and time action patterns before actual time recording occurs. This preparation enables the system to quickly identify and process employees during actual time recording without requiring complex real-time computation.
3Loss of time
If traditional time clocks are used, then the system is easy to operate, but employees must wait in queue to perform time punches causing loss of time
Solution Approach 1:
The system enables employees to self-identify and self-record time actions automatically. Instead of requiring employees to manually punch cards or swipe identifiers, the system captures images of employees, automatically identifies them through facial recognition, and records their time actions without human intervention in the recording process.
Solution Approach 2:
The patent replaces the mechanical time clock operation with automated computer vision and machine learning systems. The system uses image capturing devices and deep learning models to automatically detect, identify, and record time actions, eliminating the need for employees to physically interact with time clocks and wait for processing.
Data Source
AI summary
A system and method for a machine learning-based automated electronic time recording for personnel includes identifying, via a scene capturing device, a representation of a time recording space; identifying a body having a time recording pose within the time recording space based on an assessment of the representation of the time recording space; extracting a plurality of distinct features from the representation of the time recording space based on identifying the body having the time recording pose; executing automated user-recognition based on the extracting of the plurality of distinct features; executing automated time recording recognition based on the extracting of the plurality of distinct features; and executing automated electronic time recording, via a time recording application based on the automated user-recognition and the automated time recording recognition.