A Novel Approach for Pose Invariant Face Recognition in Surveillance Videos

Manju, D; Radha,

doi:10.1016/j.procs.2020.03.428

Cited by 18 publications

(7 citation statements)

References 14 publications

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“…Canberra distance (CD) is used to get the distance from a pair of points where the data is original and in a vector space. CD provides output in the form of actual (true) and false (false) values as shown in (20) [23]- [25].…”

Section: Canberra Distancementioning

confidence: 99%

Facial recognition using multi edge detection and distance measure

Intan

Nurdin

Pangerang

2023

IJ-AI

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Face recognition provides broad access to several public devices, so it is essential in the midst of today's technology boom. Human face recognizing has challenge in using uncomplicated and straightforward algorithms quickly, using memory specifications are not too high, otherwise the results are quality and accurate. Face recognition using combination edge detection and Canberra distance can be recommended for applications that require fast and precise access. The application of several edge detections singly has low performance, so it requires a combination technique to obtain better results. The proposed method combined several edge detections such are Robert, Prewitt, Sobel, and Canny to recognize a face image by identification and verification. As a feature extractor, the combination edge detection forms a more robust and more specific facial pattern on the contour lines. The results show that the combination accuracy outperforms other extractor features significantly. Canberra distance produces the best performance compared to Euclidean distance and Mahalanobis distance.

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Section: Canberra Distancementioning

confidence: 99%

Facial recognition using multi edge detection and distance measure

Intan

Nurdin

Pangerang

2023

IJ-AI

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“…Further, the feature vector is used for the face detection and recognition [21]. The first step is to calculate the amplitude of the gradients of each pixel of the image I(x, y) in the horizontal and vertical directions, based on which the magnitude of the gradients |G| and the orientation angle γ are obtained [13], [22], [23].…”

Section: Face Detection and Feature Vector Extractionmentioning

confidence: 99%

“…Split of the orientation range into k bins, computation of the histogram within the cell (HC), and then integration into the block by combining b1 × b2 cells, we can obtain the histogram of the block HB [22]:…”

Section: Face Detection and Feature Vector Extractionmentioning

confidence: 99%

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Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification

Vuković

Čisar

Kuk

et al. 2021

ELEKTRON ELEKTROTECH

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In a criminal investigation, along with processing forensic evidence, different investigative techniques are used to identify the perpetrator of the crime. It includes collecting and analyzing unconstrained face images, mostly with low resolution and various qualities, making identification difficult. Since police organizations have limited resources, in this paper, we propose a novel method that utilizes off-the-shelf solutions (Dlib library Histogram of Oriented Gradients-HOG face detectors and the ResNet faces feature vector extractor) to provide practical assistance in unconstrained face identification. Our experiment aimed to establish which one (if any) of the basic image enhancement techniques should be applied to increase the effectiveness. Results obtained from three publicly available databases and one created for this research (simulating police investigators’ database) showed that resizing the image (especially with a resolution lower than 150 pixels) should always precede enhancement to improve face detection accuracy. The best results in determining whether they are the same or different persons in images were obtained by applying sharpening with a high-pass filter, whereas normalization gives the highest classification scores when a single weight value is applied to data from all four databases.

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“…Face recognition has been widely used in various areas and is one of the leading computer vision challenges that has been intensively researched for two decades [1,2]. Although there have been significant breakthroughs for 2D face recognition with emerging deep learning techniques [3], recognition accuracy and stability are still a challenge because the facial appearance and surface of a person can vary significantly due to illumination conditions and changes in pose [4]. With 3D modalities, some research has focused on finding a more robust facial feature representation or descriptor based on the geometric information of a 3D face.…”

Section: Introductionmentioning

confidence: 99%

3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

Sun

et al. 2021

Electronics

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Three-dimensional (3D) face recognition has become a trending research direction in both industry and academia. However, traditional facial recognition methods carry high computational costs and face data storage costs. Deep learning has led to a significant improvement in the recognition rate, but small sample sizes represent a new problem. In this paper, we present an expression-invariant 3D face recognition method based on transfer learning and Siamese networks that can resolve the small sample size issue. First, a landmark detection method utilizing the shape index was employed for facial alignment. Then, a convolutional network (CNN) was constructed with transfer learning and trained with the aligned 3D facial data for face recognition, enabling the CNN to recognize faces regardless of facial expressions. Following that, the weighted trained CNN was shared by a Siamese network to build a 3D facial recognition model that can identify faces even with a small sample size. Our experimental results showed that the proposed method reached a recognition rate of 0.977 on the FRGC database, and the network can be used for facial recognition with a single sample.

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A Novel Approach for Pose Invariant Face Recognition in Surveillance Videos

Cited by 18 publications

References 14 publications

Facial recognition using multi edge detection and distance measure

Facial recognition using multi edge detection and distance measure

Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification

3D Expression-Invariant Face Verification Based on Transfer Learning and Siamese Network for Small Sample Size

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