Review on Facial Recognition System: Past, Present, and Future

Shree, Manu; Dev, Amita; Mohapatra, Abheejeet

doi:10.1007/978-981-19-6631-6_56

Cited by 3 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process transforms raw data into a format that can be analysed while preserving key information from the original dataset [30]. As can be seen in Figure 1, there are three primary types of features: handcrafted, non-handcrafted, and semantic [31]. Handcrafted features are further divided into global and local descriptors.…”

Section: Image Feature Extraction Using Convolutional Neural Networkmentioning

confidence: 99%

“…Handcrafted features are further divided into global and local descriptors. Global descriptors generate a feature vector representing the entire image, while local descriptors create feature vectors for specific patches within the image [31]. Non-handcrafted features are automatically learnt by ML algorithms, allowing the algorithm to identify complex patterns in the data [32].…”

Section: Image Feature Extraction Using Convolutional Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Recurrent Feature Iterative Network For Suspect Identification using Convolutional and Long Short Term Memory Approach ⋆

Shree,

Mohapatra,

Suwanwiwat

et al. 2024

Preprint

View full text Add to dashboard Cite

In suspect identification systems, facial features play a crucial role in recognising individuals. However, the challenge lies in sustaining the accuracy of the system over a long period of time, ensuring that it remains consistently high, reliable, and effective. This research introduces a novel lightweight model that requires low trainable parameters, a significantly smaller number than pre-trained models, which use millions of trainable parameters. The newly proposed recurrent feature iterative network integrates a convolutional neural network and long short-term memory in a single structure to synthesise diverse images and to effectively extract facial features. The long short-term memory-based feature-recurrent system demonstrates a significant improvement in accuracy when tested on the augmented reality face database, enhanced extended Yale B, Cohn-Kanade and extended Yale B datasets, achieving encouraging accuracy rates of 99.23%, 99.16%, 98.99%, and 97.68%, respectively. These accuracies, in general, outperform traditional baselines of 68.65%. This research advances the field by providing an innovative solution to enhance suspect identification systems through advanced facial image feature extraction, resulting in significantly improved accuracy rates.

show abstract

Section: Image Feature Extraction Using Convolutional Neural Networkmentioning

confidence: 99%

Section: Image Feature Extraction Using Convolutional Neural Networkmentioning

confidence: 99%