<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study offers a novel face recognition and classification method based on classifiers that use statistical local features. The use of ResNet has generated growing interest in a variety of areas of image processing and computer vision in recent years and demonstrated its usefulness in several applications, especially for facial image analysis, which includes tasks as varied as face detection, face recognition, facial expression analysis, demographic classification, etc. This paper is divided into two steps i.e. face recognition and classification. The first step in face recognition is automatic data cleansing which is done with the help of Multi-Task Cascaded Convolutional Neural Networks (MTCNNs) and face.evoLVe, followed by parameter changes in MTCNN to prevent dirty data. The authors next trained two models: Inception-ResNetV1, which had pre-trained weights, and Altered-ResNet (A-ResNet), which used Conv2d layers in ResNet for feature extraction and pooling and softmax layers for classifications. The authors use the best optimizer after comparing a number of them during the training phase, along with various combinations of batch and epoch. A-ResNet, the top model overall, detects 86/104 Labelled Faces in the Wild (LFW) dataset images in 0.50 seconds. The proposed approach was evaluated and received an accuracy of 91.7%. Along with this, the system achieved a training accuracy of 98.53% and a testing accuracy of 99.15% for masked face recognition. The proposed method exhibits competitive outcomes when measured against other cutting-edge algorithms and models. Finally, when it comes to why the suggested model is superior to ResNet, it may be because the A-ResNet is simpler thus it can perform at its best with little data, whereas deeper networks require higher data size.</p>
</abstract>

This study offers a novel face recognition and classification method based on classifiers that use statistical local features. The use of ResNet has generated growing interest in a variety of areas of image processing and computer vision in recent years and demonstrated its usefulness in several applications, especially for facial image analysis, which includes tasks as varied as face detection, face recognition, facial expression analysis, demographic classification, etc. This paper is divided into two steps i.e. face recognition and classification. The first step in face recognition is automatic data cleansing which is done with the help of Multi-Task Cascaded Convolutional Neural Networks (MTCNNs) and face.evoLVe, followed by parameter changes in MTCNN to prevent dirty data. The authors next trained two models: Inception-ResNetV1, which had pre-trained weights, and Altered-ResNet (A-ResNet), which used Conv2d layers in ResNet for feature extraction and pooling and softmax layers for classifications. The authors use the best optimizer after comparing a number of them during the training phase, along with various combinations of batch and epoch. A-ResNet, the top model overall, detects 86/104 Labelled Faces in the Wild (LFW) dataset images in 0.50 seconds. The proposed approach was evaluated and received an accuracy of 91.7%. Along with this, the system achieved a training accuracy of 98.53% and a testing accuracy of 99.15% for masked face recognition. The proposed method exhibits competitive outcomes when measured against other cutting-edge algorithms and models. Finally, when it comes to why the suggested model is superior to ResNet, it may be because the A-ResNet is simpler thus it can perform at its best with little data, whereas deeper networks require higher data size.

Exploring the Potential of A-ResNet in Person-Independent Face Recognition and Classification

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This study offers a novel face recognition and classification method based on classifiers that use statistical local features. The use of ResNet has generated...

Metrics	Value
Accuracy	0.7884615384615384
Time	38s

Study	Accuracy (%)
Zhou et al. [1]	99.50%
Iqbal et al. [2]	96.40%
Balaban [3]	99.63%
Sun et al. [4]	67%

Metrics	Value
Accuracy	0.9169230769230769
Time	50s

Epochs	Batch size	True Positive	Train FPS
24	64	70	151.9
24	128	81	170.4
32	128	85	254.4
32	256	76	209.8
64	256	76	194.3

Epochs	Batch size	True Positive	Train FPS
10	16	20	426.4
24	16	25	421.7
24	32	40	278.6
24	64	74	151.2
32	64	70	160.7
24	128	79	148.9
32	128	76	232.4
24	256	69	182.5
32	256	76	192.8
64	256	75	154.3

Exploring the Potential of A-ResNet in Person-Independent Face Recognition and Classification

Published Online: Aug 16, 2023

Page range: 12 - 19

DOI: https://doi.org/10.2478/ijanmc-2023-0052

Keywords
Face Recognition, Face Imag, Local Binary Patterns, Labelled Faces In The Wild

© 2023 Ahmed Mahdi Obaid et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Results for ResNet

Accuracy achieved

Results for A-ResNet

Records of combination for A-ResNet

Records of combination for ResNet

Recognition rate based on LFW database

Recognition at 45 px	Correct Times	Wrong Times	Correct Image Accuracy	Incorrect Image Accuracy
Front facing	87	17	83.65%	16.35%
Facing 30’ Right	89	15	85.57%	14.43%
Facing 30’ Left	91	13	87.50%	12.5%

Exploring the Potential of A-ResNet in Person-Independent Face Recognition and Classification

Published Online: Aug 16, 2023

Page range: 12 - 19

DOI: https://doi.org/10.2478/ijanmc-2023-0052

KeywordsFace Recognition, Face Imag, Local Binary Patterns, Labelled Faces In The Wild

© 2023 Ahmed Mahdi Obaid et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Results for ResNet

Accuracy achieved

Results for A-ResNet

Records of combination for A-ResNet

Records of combination for ResNet

Recognition rate based on LFW database

Keywords
Face Recognition, Face Imag, Local Binary Patterns, Labelled Faces In The Wild