Face Recognition on a Smart Image Sensor Using Local Gradients

Valenzuela, Wladimir E.; Soto, Javier E.; Zarkesh-Ha, Payman; Figueroa, Miguel

doi:10.3390/s21092901

Cited by 8 publications

(5 citation statements)

References 86 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computer vision systems have been extensively researched in early face-detection studies [ 34 ]. Despite many extensive studies in recent years, based on the technique proposed by Viola–Jones [ 7 ], there exists insufficient practical results on face detection.…”

Section: Related Workmentioning

confidence: 99%

Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach

Dilnoza

Abdusalomov

Mukhiddinov

et al. 2023

Sensors

View full text Add to dashboard Cite

Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed gradients, mainly rely on features that had been manually developed from particular images. However, these techniques are unable to correctly synthesize images taken in untamed situations. However, deep learning’s quick development in computer vision has also sped up the development of a number of deep learning-based face detection frameworks, many of which have significantly improved accuracy in recent years. When detecting faces in face detection software, the difficulty of detecting small, scale, position, occlusion, blurring, and partially occluded faces in uncontrolled conditions is one of the problems of face identification that has been explored for many years but has not yet been entirely resolved. In this paper, we propose Retina net baseline, a single-stage face detector, to handle the challenging face detection problem. We made network improvements that boosted detection speed and accuracy. In Experiments, we used two popular datasets, such as WIDER FACE and FDDB. Specifically, on the WIDER FACE benchmark, our proposed method achieves AP of 41.0 at speed of 11.8 FPS with a single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are results among one-stage detectors. Then, we trained our model during the implementation using the PyTorch framework, which provided an accuracy of 95.6% for the faces, which are successfully detected. Visible experimental results show that our proposed model outperforms seamless detection and recognition results achieved using performance evaluation matrices.

show abstract

Section: Related Workmentioning

confidence: 99%

Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach

Dilnoza

Abdusalomov

Mukhiddinov

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…To reduce the impact of pixel-level processing on the pixel area and photodetector utilization, a number of works have been proposed in the literature implementing computation at the column level [ 48 , 49 , 50 , 51 , 52 ]. Young et al.…”

Section: Related Workmentioning

confidence: 99%

“…[ 51 ] presented an SIS for feature extraction that computed the convolution between the image and a

kernel, with the programmable weights represented as analog currents and the pixels as pulse-width modulated voltages. In our own previous work [ 52 , 53 ], we combined pixel- and column-level analog computation during photocurrent integration using a custom digital processor. In [ 52 ], the analog circuits were used to compute a simplified version of LBP for use by the digital coprocessor in performing facial recognition on images.…”

Section: Related Workmentioning

confidence: 99%

“…In our own previous work [ 52 , 53 ], we combined pixel- and column-level analog computation during photocurrent integration using a custom digital processor. In [ 52 ], the analog circuits were used to compute a simplified version of LBP for use by the digital coprocessor in performing facial recognition on images. In [ 53 ], we used pixel-level analog memory to compute frame differences and motion estimation, with the digital processor using a connected components algorithm to detect objects in motion.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A CMOS Image Readout Circuit with On-Chip Defective Pixel Detection and Correction

López-Portilla

Valenzuela

Zarkesh-Ha

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Images produced by CMOS sensors may contain defective pixels due to noise, manufacturing errors, or device malfunction, which must be detected and corrected at early processing stages in order to produce images that are useful to human users and image-processing or machine-vision algorithms. This paper proposes a defective pixel detection and correction algorithm and its implementation using CMOS analog circuits, which are integrated with the image sensor at the pixel and column levels. During photocurrent integration, the circuit detects defective values in parallel at each pixel using simple arithmetic operations within a neighborhood. At the image-column level, the circuit replaces the defective pixels with the median value of their neighborhood. To validate our approach, we designed a 128×128-pixel imager in a 0.35μm CMOS process, which integrates our defective-pixel detection/correction circuits and processes images at 694 frames per second, according to post-layout simulations. Operating at that frame rate, our proposed algorithm and its CMOS implementation produce better results than current state-of-the-art algorithms: it achieves a Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF) of 45 dB and 198.4, respectively, in images with 0.5% random defective pixels, and a PSNR of 44.4 dB and IEF of 194.2, respectively, in images with 1.0% random defective pixels.

show abstract

“…In this work, a novel technique for the fusion and accumulation of multi-view blurred images of invisible targets in dense fog is proposed, where the close object within visibility range is visible, while the distant target beyond visibility is invisible. In this situation, pixels acting as smart pixels [23,24] carry information for the locations and pointing directions for distributed recording cameras, which can be calibrated with the assistance of multiview visible images of the close object. By using such position and pointing direction parameters, the extrinsic parameter matrices are calculated and applied to the image fusion of the invisible target out of the visible range.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Optical Image Fusion and Reconstruction for Defogging without a Prior In-Plane

Huang

Liu

et al. 2021

Photonics

View full text Add to dashboard Cite

Image fusion and reconstruction from muldti-images taken by distributed or mobile cameras need accurate calibration to avoid image mismatching. This calibration process becomes difficult in fog when no clear nearby reference is available. In this work, the fusion of multi-view images taken in fog by two cameras fixed on a moving platform is realized. The positions and aiming directions of the cameras are determined by taking a close visible object as a reference. One camera with a large field of view (FOV) is applied to acquire images of a short-distance object which is still visible in fog. This reference is then adopted to the calibration of the camera system to determine the positions and pointing directions at each viewpoint. The extrinsic parameter matrices are obtained with these data, which are applied for the image fusion of distant images captured by another camera beyond visibility. The experimental verification was carried out in a fog chamber and the technique is shown to be valid for imaging reconstruction in fog without a prior in-plane. The synthetic image, accumulated and averaged by ten-view images, is shown to perform potential applicability for fog removal. The enhanced structure similarity is discussed and compared in detail with conventional single-view defogging techniques.

show abstract

Face Recognition on a Smart Image Sensor Using Local Gradients

Cited by 8 publications

References 86 publications

Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach

Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach

A CMOS Image Readout Circuit with On-Chip Defective Pixel Detection and Correction

Multi-View Optical Image Fusion and Reconstruction for Defogging without a Prior In-Plane

Contact Info

Product

Resources

About