Biased face patching approach for age invariant face recognition using convolutional neural network

Nimbarte, Mrudula; Bhoyar, Kishor K.

doi:10.1504/ijista.2020.107216

Cited by 6 publications

(3 citation statements)

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“…There are many models and methods that can be used in facial recognition, one of which is deep learning. Convolutional neural networks [18] is one of the many methods available in deep learning that can be used for facial recognition [19]- [21] both in real-time [22] or not in real-time [23]. There is a study that builds a model based on normalized features extracted by deep CNN [24].…”

Section: Selection Stagementioning

confidence: 99%

Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

Budiarsa,

Wardoyo,

Musdholifah

2023

IJECE

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Face recognition technology has been used in many ways, such as in the authentication and identification process. The object raised is a piece of face image that does not have complete facial information (occluded face), it can be due to acquisition from a different point of view or shooting a face from a different angle. This object was raised because the object can affect the detection and identification performance of the face image as a whole. Deep leaning method can be used to solve face recognition problems. In previous research, more focused on face detection and recognition based on resolution, and detection of face. Mask region convolutional neural network (mask R-CNN) method still has deficiency in the segmentation section which results in a decrease in the accuracy of face identification with incomplete face information objects. The segmentation used in mask R-CNN is fully convolutional network (FCN). In this research, exploration and modification of many FCN parameters will be carried out using the CNN backbone pooling layer, and modification of mask R-CNN for face identification, besides that, modifications will be made to the bounding box regressor. it is expected that the modification results can provide the best recommendations based on accuracy.

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Section: Selection Stagementioning

confidence: 99%

Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

Budiarsa,

Wardoyo,

Musdholifah

2023

IJECE

View full text Add to dashboard Cite

show abstract

“…Finally, recognition focused on the rest of body parts can be identified skeletal posture [61], neuronal illness or social actions [62], as social touch [63]. The estimation of age and/or gender can be done through facial recognition [64], [65] or through hand gestures [66], [67]. Apps estimating ethnicity [68], beauty [69] and body constitution [70] were also detected exclusively through facial recognition.…”

Section: ) Applicationsmentioning

confidence: 99%

A Survey on Intelligent Gesture Recognition Techniques

et al. 2022

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Gesture recognition is an ideal means of interaction because it allows users not to have to make contact with any surface, which is a safe and hygienic means, especially in the pandemic situation that is occurring worldwide. However, gesture recognition is not a new discipline and it has been researched for many years but this type of interaction has not succeeded in replacing the keyboard and mouse. It is very useful to know about the advances that are being made with artificial intelligence in gesture recognition to be able to perform a more robust and reliable gesture recognition with a low response time. As it is, deep learning is being integrated into various areas to increase improvement in performance and one such area is artificial intelligence. In this way, there is the possibility that in the future the recognition of gestures will be a viable option as a means of daily interaction for the user and the main objective of this paper is to contribute to that process. For this reason, this study has analyzed 571 papers related to gesture recognition and artificial intelligence. This analysis has extracted relevant information related to scientific production, such as the most productive authors and journals or the most pertinent articles on the subject. Furthermore, we have developed our own model, which shows the relationship between the types of gesture recognition and the artificial intelligence techniques that have been applied for this task.

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“…Face detection plays a role in the face localization process, namely the process of finding the size and position of a face in an image, while the Face identification process determines the class of the object. mask R-CNN is usually used for detection [16], [18]- [22] and recognition [23]- [27] of objects. Mask R-CNN extends Faster R-CNN by adding a new branch for predicting mask objects in parallel with the existing branch for bounding box recognition.…”

Section: Introductionmentioning

confidence: 99%

Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network

Budiarsa,

Wardoyo,

Musdholifah

2024

IJECE

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Face recognition entails detecting and identifying facial attributes. Mask region convolutional neural network (R-CNN) method is a prominent approach, while prior research predominantly delved into refining loss functions and perfecting object and face detection, recognizing, and identifying faces using imperfect data remained relatively unexplored. This study focuses on an occluded dataset comprising Indonesian faces, wherein 'occluded' denotes facial data that lacks complete visibility-encompassing instances where objects obscure faces or are partially cropped. This investigation involves a deliberate experiment that tailors the intersection over union (IoU) of the region proposal network (RPN) to suit the nuances of occluded Indonesian faces, thereby augmenting accuracy in recognition and segmentation tasks. The innovation IoU in the strategic utilization of Anchors, which involves the exclusion of anchors falling beyond the image borders to optimize computational efficiency. The outcomes of this research are striking; it showcases a remarkable 14.75%, 10.9%, and 12.97% surge based on mean average precision (mAP), mean average recall (mAR), and F1-Scores compared to the conventional Mask R-CNN approach. Notably, our proposed model elevates the average accuracy by 10% to 15% and decreases running time by 21%, a noteworthy enhancement compared to the preceding model. This progress is substantiated by validation utilizing 300 instances dataset, reinforcing the robustness of our approach.

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Biased face patching approach for age invariant face recognition using convolutional neural network

Cited by 6 publications

References 0 publications

Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

A Survey on Intelligent Gesture Recognition Techniques

Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network

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