Real-Time Facial Recognition Using SURF-FAST

Setta, Showmik; Sinha, Shreyashee; Mishra, Monalisa; Choudhury, Prasenjit

doi:10.1007/978-981-16-2937-2_32

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…Points of interest do not disappear due to changes in lighting, perspective or noise. They describe local features of objects, and there are a variety of representations, commonly used are SIFT [8] and SURF [17, 18]. SIFT is invariant to scale and robust, but it has higher feature dimensions and more feature points, which results in more computation.…”

Section: Methodsmentioning

confidence: 99%

Object recognition based on shape interest points descriptor

Zhang,

2024

Electronics Letters

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Considering the defect of object recognition with single global feature or local feature, in this letter the authors propose a shape interest points descriptor (SIPD) for object recognition. Particularly, the authors extract the shape features by the improved HU moments and then interest points by Speeded up Robust Feature (SURF). Object recognition is carried out by similarity measure. Because the influence of the improved HU moments and SURF for object recognition is different, the two different similarity measures are fused effectively by using different weight factors. Experimental results show that the author’ proposed method is effective and robust to the changes of scale, viewing angle, illumination and noise when compared with the other representative methods.

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Section: Methodsmentioning

confidence: 99%

Object recognition based on shape interest points descriptor

Zhang,

2024

Electronics Letters

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“…Here, the local feature, SURF [42], is preferred as part of the handcrafted feature extraction for model development because it provides information about each keypoint in the image and is robust against noise, distortions, and scale space invariance. SURF is appropriate for real-time applications [43,44] because it offers fast computation [45] owing to the use of integral images and box filters.…”

Section: Surf Featuresmentioning

confidence: 99%

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

Alshayeji

2023

Mach. Learn.: Sci. Technol.

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Epilepsy is one of the most debilitating neurological diseases that abruptly alters a person's way of life. Manual diagnosis is a laborious and time-consuming task prone to human error; therefore, automating this task by developing an intelligent system is necessary. Existing deep learning (DL) models require high training time, large datasets, and machines with more memory and processing power. In addition, owing to the black-box nature of DL models, no one can determine the features that the network prefers for classification decisions. To overcome these challenges, this paper proposes an accurate, automatic, and fast intelligent system for epilepsy detection using a computer-aided diagnosis (CAD)-2D machine learning (ML) framework. Existing ML models struggle to produce reliable and acceptable diagnostic results owing to the low amplitude and nonstationary nature of electroencephalograms (EEG), particularly in clinical situations where environmental influences are almost impossible to eliminate. The proposed model was built on the CHB-MIT dataset, and it represents the first study that employs the speeded-up robust feature (SURF) bag of features (BOF) technique for this application, which generates local features from the spectrogram images of the respective 1D EEG signal inputs. In addition, DL features were extracted from the spectrogram images for model performance comparisons. Both features were used separately to train the ML classifiers. Implementing SURF offers fast computation and makes the model invariant to distortions, noise, scaling, and so on. Therefore, the proposed model is more suitable for real-time applications, proposed ML framework provides an enhanced accuracy of 99.78% from the SVM-RBF classifier, along with 99.56% sensitivity, 100% specificity, and an error rate of 0.22%. The higher detection accuracy demonstrated the effectiveness of the proposed framework for medical disease diagnosis applications.

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“…Tran et al have proposed a method that detects the face from collaborating environment along with a different variation of poses [ 174 ]. Researchers also identify the person from the live video by providing the label [ 153 ]. The face detection is also done based on the complexion of people using the hybrid approach in which RGB and YCbCr colour spaces are combined for face detection from surveillance videos [ 61 ].…”

Section: Applications Of Object Detectionmentioning

confidence: 99%

Tools, techniques, datasets and application areas for object detection in an image: a review

Kaur

Singh

2022

Multimed Tools Appl

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Object detection is one of the most fundamental and challenging tasks to locate objects in images and videos. Over the past, it has gained much attention to do more research on computer vision tasks such as object classification, counting of objects, and object monitoring. This study provides a detailed literature review focusing on object detection and discusses the object detection techniques. A systematic review has been followed to summarize the current research work’s findings and discuss seven research questions related to object detection. Our contribution to the current research work is (i) analysis of traditional, two-stage, one-stage object detection techniques, (ii) Dataset preparation and available standard dataset, (iii) Annotation tools, and (iv) performance evaluation metrics. In addition, a comparative analysis has been performed and analyzed that the proposed techniques are different in their architecture, optimization function, and training strategies. With the remarkable success of deep neural networks in object detection, the performance of the detectors has improved. Various research challenges and future directions for object detection also has been discussed in this research paper.

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Real-Time Facial Recognition Using SURF-FAST

Cited by 6 publications

References 32 publications

Object recognition based on shape interest points descriptor

Object recognition based on shape interest points descriptor

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

Tools, techniques, datasets and application areas for object detection in an image: a review

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