Abstract
Identity management system in most academic and office environments is presently achieved primarily by a manual method where the user has to input their attendance into the system. The manual method sometimes results in human error and makes the process less efficient and time-consuming. The proposed system highlights the implementation and design of a smart face identification-based management system while taking into account both the background luminosity and distance. This system detects and recognizes the person and marks their attendance with the timestamp. In this methodology, the face is initially resized to 3 different sizes of 256, 384, and 512 pixels for multiscale testing. The overall outcome size descriptor is the overall mean for these characteristic vectors, and the deep convolution neural network calculates 22 facial features in 128 distinct embeddings in 22-deep network layers. The pose of the 2D face from −15 to +15° provides identification with 98% accuracy in low computation time. Another feature of the proposed system is that it is able to accurately perform identification with an accuracy of 99.92% from a distance of 5 m under optimal light conditions. The accuracy is also dependent on the light intensity where it varies from 96% to 99% under 100 to 1000 lumen/m2, respectively. The presented model not only improves accuracy and identity under realistic conditions but also reduces computation time.
Highlights
In many public and educational sectors, the management system is mandatory for analyzing the performance of candidates
When there are a lot of individuals in an organization or institute, it becomes significantly more difficult to mark their presence through the manual procedure and it is time-consuming. e conventional marking method is obsolete, and in such systems, identification is recorded with traditional approaches that include registers and sheets whereas more advanced methods like RFID and biometric encounter the difficulty of time wastage and are significantly more complicated where you have to wait in line to swipe the RFID card or put your thumb on a scanner which can be a quick way of spreading unwanted diseases
The biometric system needs more hardware, and its maintenance is difficult. e automatic system can resolve a crucial issue within the manual one that occurs when a person transfers the information from the sheet into the system. e face identification method has many steps which include capture, extraction, comparison, and matchmaking
Summary
In many public and educational sectors, the management system is mandatory for analyzing the performance of candidates. This research resulted in an approximately 95% facial identification accuracy in the thermal and appearance-based data Another facial recognition approach was created with a database of 50 people along with 10 images per individual, which offered authentic evidence for facial recognition within the IR spectrum [11]. While within the decision level, the precision of two-person matching within the ROI and visible spectrum is computed, which makes the model more complex Another problem in face recognition is time-lapse; i.e., the performance of an algorithm decreases as time passes between training and test data without taking into account the scanning conditions. Title “Image to Bengali Caption Generation Using Deep CNN and Bidirectional Gated
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