trinamiX Face Authentication Tech Receives IIFAA Certificate

March 15, 2022 By Leave a Comment

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trinamiX Face Authentication fulfills the biometric security requirements defined by the International Internet Finance Authentication Alliance (IIFAA). After recently announcing the fulfilment of the FIDO Alliance and Android Biometric Security standards, the German tech company is now topping it off with their newest certification. trinamiX GmbH, a subsidiary of BASF SE, has thereby proven to be suited for integration in digital payment processes with particularly high security demands. Their solution is the first to pass these tests while the hardware is invisibly mounted behind OLED. IIFAA’s standard is adhered to by leading players in the FinTech industry and has the widest market coverage in China.

trinamiX Face Authentication became the world’s first to pass all these tests and to provide “financial-level security”2 while all hardware was integrated behind a full-screen display. “We’ve finally received living proof of our capability to raise the bar of biometric security,” Stefan Metz, Director 3D Imaging Business at trinamiX, stated. “Our solution can mean a breakthrough to the world of digital payment, allowing users to better trust in and benefit from digital financial services.” The innovative strength of this solution unveils itself through a closer look into the underlying technology: trinamiX Face Authentication introduces a one-of-a-kind liveness check to the authentication process in order to tell whether the object in front of the camera is an actual human-being. In addition to checking the presented face for three-dimensional depth, it reliably detects skin versus other materials. Thanks to skin detection, not even a hyperrealistic replica of a user’s face can trick the system – while common biometric authentication solutions are still prone to these fraud attempts.

The latest test result of trinamiX Face Authentication, issued by IIFAA’s testing agency, certifies that the solution complies with the IIFAA Biometric Face Security Test Requirement, which is an established authentication standard for digital financial services.

Full article: https://trinamixsensing.com/news-events/press/trinamix-face-authentication-behind-oled-earns-international-biometric-security-certificate-by-iifaa/

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SmartSens 50MP Ultra-High-Resolution Image Sensor

March 15, 2022 By Leave a Comment

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SmartSens has launched an ultra high resolution image sensor based on a 22nm process. SC550XS is their first 50MP ultra-high resolution image sensor with a 1.0μm pixel size. The new product adopts the advanced 22nm HKMG Stack process as well as SmartSens’ multiple proprietary technologies, including SmartClarity®-2 technology, SFCPixel® technology and PixGain HDR® technology to enable excellent imaging performance. In addition, it can achieve 100% all pixel all direction auto focus coverage via AllPix ADAF® technology and is equipped with MIPI C-PHY 3.0Gsps high-speed data transmission interface. The product is designed to address the requirements of flagship smartphone main camera in terms of night vision full-color imaging, high dynamic range, and low power consumption.










Full press release: https://www.smartsenstech.com/en/page?id=179

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Low Power Edge-AI Vision Sensor

March 15, 2022 By Leave a Comment

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Another interesting article from the upcoming tinyML conference. This one is titled "P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications" and is work done by a team from University of Southern California.

The demand to process vast amounts of data generated from state-of-the-art high resolution cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such cameras are usually captured in the form of analog voltages by a sensor pixel array, and then converted to the digital domain for subsequent AI processing using analog-to-digital converters (ADC). Recent research has tried to take advantage of massively parallel low-power analog/digital computing in the form of near- and in-sensor processing, in which the AI computation is performed partly in the periphery of the pixel array and partly in a separate on-board CPU/accelerator. Unfortunately, high-resolution input images still need to be streamed between the camera and the AI processing unit, frame by frame, causing energy, bandwidth, and security bottlenecks. To mitigate this problem, we propose a novel Processing-in-Pixel-in-memory (P2M) paradigm, that customizes the pixel array by adding support for analog multi-channel, multi-bit convolution and ReLU (Rectified Linear Units). Our solution includes a holistic algorithm-circuit co-design approach and the resulting P2M paradigm can be used as a drop-in replacement for embedding memory-intensive first few layers of convolutional neural network (CNN) models within foundry-manufacturable CMOS image sensor platforms. Our experimental results indicate that P2M reduces data transfer bandwidth from sensors and analog to digital conversions by ~21x, and the energy-delay product (EDP) incurred in processing a MobileNetV2 model on a TinyML use case for visual wake words dataset (VWW) by up to ~11x compared to standard near-processing or in-sensor implementations, without any significant drop in test accuracy.






arXiv preprint: https://arxiv.org/pdf/2203.04737.pdf

tinyML conference information: https://www.tinyml.org/event/summit-2022/

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A Curious Observation about 1-bit Quanta Image Sensors Explained

March 15, 2022 By Leave a Comment

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Dr. Stanley Chan (Purdue University) has a preprint out titled "On the Insensitivity of Bit Density to Read Noise in One-bit Quanta Image Sensors" on arXiv. This paper presents a rigorous theoretical analysis of an intuitive but curious observation that was first made in the paper by E. Fossum titled "Analog read noise and quantizer threshold estimation from Quanta Image Sensor Bit Density."

Why is the quanta image sensor bit density insensitive to read noise at high enough exposure values?

The one-bit quanta image sensor is a photon-counting device that produces binary measurements where each bit represents the presence or absence of a photon. In the presence of read noise, the sensor quantizes the analog voltage into the binary bits using a threshold value q. The average number of ones in the bitstream is known as the bit-density and is often the sufficient statistics for signal estimation. An intriguing phenomenon is observed when the quanta exposure is at unity and the threshold is q=0.5. The bit-density demonstrates a complete insensitivity as long as the read noise level does not exceeds a certain limit. In other words, the bit density stays at a constant independent of the amount of read noise. This paper provides a mathematical explanation of the phenomenon by deriving conditions under which the phenomenon happens. It was found that the insensitivity holds when some forms of the symmetry of the underlying Poisson-Gaussian distribution holds.



The paper concludes:

The insensitivity of the bit density of a 1-bit quanta image sensor is analyzed. It was found that for a quanta exposure θ = 1 and an analog voltage threshold q = 0.5, the bit density D is nearly a constant whenever the read noise satisfies the condition σ ≤ 0.4419. The proof is derived by exploiting the symmetry of the Gaussian cumulative distribution function, and the symmetry of the Poisson probability mass function at the threshold k = 0.5. An approximation scheme is introduced to provide a simplified estimate where σ ≤ 1/√2π = 0.4. In general, the analysis shows that the insensitivity of the bit density is more of a (very) special case of the 1-bit quantized Poisson-Gaussian statistics. Insensitivity can be observed when the quanta exposure θ is an integer and the threshold is q = θ−0.5. As soon as the pair (θ, q) deviates from this configuration, the insensitivity will no longer appear.

Complete article can be downloaded here: https://arxiv.org/pdf/2203.06086

An early-access version of Eric's paper is available here: https://ieeexplore.ieee.org/document/9729893

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High Resolution MEMS LiDAR Paper in Nature Magazine

March 14, 2022 By Leave a Comment

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Researches from the Integrated Photonics Lab at UC-Berkeley recently published a paper titled "A large-scale microelectromechanical-systems-based silicon photonics LiDAR" proposing a CMOS-compatible high-resolution scanning MEMS LiDAR system.

Three-dimensional (3D) imaging sensors allow machines to perceive, map and interact with the surrounding world. The size of light detection and ranging (LiDAR) devices is often limited by mechanical scanners. Focal plane array-based 3D sensors are promising candidates for solid-state LiDARs because they allow electronic scanning without mechanical moving parts. However, their resolutions have been limited to 512 pixels or smaller. In this paper, we report on a 16,384-pixel LiDAR with a wide field of view (FoV, 70° × 70°), a fine addressing resolution (0.6° × 0.6°), a narrow beam divergence (0.050° × 0.049°) and a random-access beam addressing with sub-MHz operation speed. The 128 × 128-element focal plane switch array (FPSA) of grating antennas and microelectromechanical systems (MEMS)-actuated optical switches are monolithically integrated on a 10 × 11-mm2 silicon photonic chip, where a 128 × 96 subarray is wire bonded and tested in experiments. 3D imaging with a distance resolution of 1.7 cm is achieved with frequency-modulated continuous-wave (FMCW) ranging in monostatic configuration. The FPSA can be mass-produced in complementary metal–oxide–semiconductor (CMOS) foundries, which will allow ubiquitous 3D sensors for use in autonomous cars, drones, robots and smartphones.





Full article available here: https://www.nature.com/articles/s41586-022-04415-8.pdf

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Ultra-Low Power Camera for Intrusion Monitoring

March 14, 2022 By Leave a Comment

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An interesting paper titled "Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring"  will be presented at the upcoming tinyML Research Symposium. This symposium is colocated with the tinyML Summit 2022 to be held from March 28-30 in Burlingame, CA (near SFO).

Millimeter-scale embedded sensing systems have unique advantages over larger devices as they are able to capture, analyze, store, and transmit data at the source while being unobtrusive and covert. However, area-constrained systems pose several challenges, including a tight energy budget and peak power, limited data storage, costly wireless communication, and physical integration at a miniature scale. This paper proposes a novel 6.7×7×5mm imaging system with deep-learning and image processing capabilities for intelligent edge applications, and is demonstrated in a home-surveillance scenario. The system is implemented by vertically stacking custom ultra-low-power (ULP) ICs and uses techniques such as dynamic behavior-specific power management, hierarchical event detection, and a combination of data compression methods. It demonstrates a new image-correcting neural network that compensates for non-idealities caused by a mm-scale lens and ULP front-end. The system can store 74 frames or offload data wirelessly, consuming 49.6μW on average for an expected battery lifetime of 7 days.

Preprint is up on arXiv: https://arxiv.org/abs/2203.04496



Personally, I find such work quite fascinating. With recent advances in learning based approaches for computer vision, we're seeing a "race to the top" --- larger neural networks, humongous datasets, and even beefier GPUs drawing 100's of watts of power. But, on the other hand, there's also a "race to the bottom" driven by edge computing/IoT applications that are extremely resource constrained --- microwatts of power, low image resolutions, and splitting hairs over every bit, every byte of data transferred.

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Artilux Announces CMOS IR Sensor for Mobile Digital Health Applications

March 13, 2022 By Leave a Comment

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Hsinchu, Taiwan, March 8th 2022 – Artilux, the leader in CMOS-based SWIR optical sensing technology, demonstrated a multi-spectral optical sensing platform compatible with NIR/SWIR vertical-cavity surface-emitting laser (VCSEL) arrays, light emitting diodes (LED), and CMOS-based GeSi (Germanium-Silicon) sensors. This compact optical sensing platform is the industry-leading solution targeted to embrace the rapidly growing TWS and wearables markets in addition to unlock diversified scenarios in digital health.

In light of the increasingly popular wide spectrum (NIR/SWIR) optical sensing applications starting from vital sign monitoring in smartwatches to skin detection in TWS earbuds, cost-effective and energy-efficient optical components including LED, VCSEL, edge-emitting lasers, and SWIR sensors have become the crucial factors to meet such rising user demands. The widely discussed skin detection function in TWS earbuds requires SWIR sensors to perform precise in-ear detection and to deliver seamless listening experiences, while at the same time sustaining long battery life. Such product requires SWIR wavelength, lower power-consumption, lower cost, smaller size with higher sensitivity. The announcement aims to deliver a compact and cost-effective multi-spectral optical sensing solution, by incorporating Artilux’s CMOS-based ultra-sensitive SWIR GeSi sensors with the capability to integrate AFE (analog front end) and digital function into a single chip, together with high-performance VCSEL arrays at 940nm and 1380nm supplied by Lumentum.

 

Although the press release does not mention any technical specifications it may be worth referring to an ISSCC paper from 2020 published by a team from Artilux that described a Ge-on-Si technology. The paper is titled "An Up-to-1400nm 500MHz Demodulated Time-of-Flight Image Sensor on a Ge-on-Si Platform" (https://doi.org/10.1109/ISSCC19947.2020.9063107).

 




Press Release: https://www.artiluxtech.com/resources/news/1014


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Telluride Neuromorphic Workshop 2022

March 11, 2022 By Leave a Comment

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The 2022 edition of the Telluride Neuromorphic Workshop series will be held in-person June 26 to July 16 in beautiful Telluride, Colorado. The topics of interest are broadly in "neuromorphic engineering" with neuromorphic vision sensors (including event cameras and other "spiking"-based vision sensors) being key areas of interest.

Neuromorphic engineers design and fabricate artificial neural systems whose organizing principles are based on those of biological nervous systems. Over the past 27 years, the neuromorphic engineering research community focused on the understanding of low-level sensory processing and systems infrastructure; efforts are now expanding to apply this knowledge and infrastructure to addressing higher-level problems in perception, cognition, and learning. In this 3-week intensive workshop and through the Institute for Neuromorphic Engineering (INE), the mission is to promote interaction between senior and junior researchers; to educate new members of the community; to introduce new enabling fields and applications to the community; to promote ongoing collaborative activities emerging from the Workshop, and to promote a self-sustaining research field.

The workshop will be organized in four topic areas: 

  • Neuromorphic Tactile Exploration (Enhance the tactile exploration capabilities of robots)
  • Lifelong Learning at Scale: From Neuroscience Theory to Robotic Applications (Apply neuro-inspired principles of lifelong learning to autonomous systems.)
  • Cross-modality brain signals: auditory, visual and motor 
  • Neuromorphics Tools, Techniques and Hardware (SpiNNaker 2 and FPAAs)

Researchers from academia, industry and national labs are all encouraged to apply... 

... in particular if they are prepared to work on specific projects, talk about their own work or bring demonstrations to Telluride (e.g. robots, chips, software). 

An application is required to attend, and financial support is available. Application deadline is April 8, 2022.

Call for applications.

Application submission page.

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Privacy-Aware Cameras for Human Pose Recognition

March 9, 2022 By Leave a Comment

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Carlos Hinojosa, Juan Carlos Niebles and Henry Arguello published an article titled "Learning Privacy-preserving Optics for Human Pose Estimation" in the 2021 International Conference on Computer Vision which was held virtually in October 2021. This is a collaboration between Universidad Industrial de Santander (Colombia) and Stanford University (USA).




The widespread use of always-connected digital cameras in our everyday life has led to increasing concerns about the users’ privacy and security. How to develop privacy-preserving computer vision systems? In particular, we want to prevent the camera from obtaining detailed visual data that may contain private information. However, we also want the camera to capture useful information to perform computer vision tasks. Inspired by the trend of jointly designing optics and algorithms, we tackle the problem of privacy-preserving human pose estimation by optimizing an optical encoder (hardware-level protection) with a software decoder (convolutional neural network) in an end-to-end framework. We introduce a visual privacy protection layer in our optical encoder that, parametrized appropriately, enables the optimization of the camera lens’s point spread function (PSF). We validate our approach with extensive simulations and a prototype camera. We show that our privacy-preserving deep optics approach successfully degrades or inhibits private attributes while maintaining important features to perform human pose estimation.


They take a "deep-optics" approach --- a learning-based approach where a neural network is used not only to recognize the human pose, but also to train a privacy-preserving point-spread-function (PSF). The neural network is trained to strike a balance between two competing requirements: (a) hiding scene information so that human aces are not recognizable in the RGB images (even after image deblurring), while ensuring that (b) the PSF distortions aren't so strong that the pose-estimation task becomes impossible.



Their results look quite promising, and they even built a proof-of-concept hardware prototype using a wavefront modulator. Notice that the human faces are not recognizable in the RGB images, but the "match-stick" skeletons are still reliably picked out by the algorithm.




More details are in the open access paper and accompanying supplementary document and video available here: https://carloshinojosa.me/project/privacy-hpe/ 

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Privacy-Aware Cameras for Human Pose Recognition

March 9, 2022 By Leave a Comment

Image Sensors World        Go to the original article...

Carlos Hinojosa, Juan Carlos Niebles and Henry Arguello published an article titled "Learning Privacy-preserving Optics for Human Pose Estimation" in the 2021 International Conference on Computer Vision which was held virtually in October 2021. This is a collaboration between Universidad Industrial de Santander (Colombia) and Stanford University (USA).




The widespread use of always-connected digital cameras in our everyday life has led to increasing concerns about the users’ privacy and security. How to develop privacy-preserving computer vision systems? In particular, we want to prevent the camera from obtaining detailed visual data that may contain private information. However, we also want the camera to capture useful information to perform computer vision tasks. Inspired by the trend of jointly designing optics and algorithms, we tackle the problem of privacy-preserving human pose estimation by optimizing an optical encoder (hardware-level protection) with a software decoder (convolutional neural network) in an end-to-end framework. We introduce a visual privacy protection layer in our optical encoder that, parametrized appropriately, enables the optimization of the camera lens’s point spread function (PSF). We validate our approach with extensive simulations and a prototype camera. We show that our privacy-preserving deep optics approach successfully degrades or inhibits private attributes while maintaining important features to perform human pose estimation.


They take a "deep-optics" approach --- a learning-based approach where a neural network is used not only to recognize the human pose, but also to train a privacy-preserving point-spread-function (PSF). The neural network is trained to strike a balance between two competing requirements: (a) hiding scene information so that people's faces are not recognizable in the RGB images (even after image deblurring), while ensuring that (b) the PSF distortions aren't so strong that the pose-estimation task becomes impossible.



Their results look quite promising, and they even built a proof-of-concept hardware prototype using a wavefront modulator. Notice that the human faces are not recognizable in the RGB images, but the "match-stick" skeletons are still reliably picked out by the algorithm.




More details are in the open access paper and accompanying supplementary document and video available here: https://carloshinojosa.me/project/privacy-hpe/ 

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