Abstract

Deployment of Face Recognition systems on the edge has seen significant growth due to advancements in hardware design and efficient neural architectures. However, tailoring SOTA Face Recognition solutions to a specific edge device is still not easy and is vastly unexplored. Although, benchmark data is available for some combinations of model, device, and framework, it is neither comprehensive nor scalable. We propose an approximation to determine the relationship between a model and its inference time in an edge deployment scenario. Using a small number of data points, we are able to predict the throughput of custom models in an explainable manner. The prediction errors are small enough to be considered noise in observations. We also analyze which approaches are most efficient and make better use of hardware in terms of accuracy and error rates to gain a better understanding of their behaviour. Related & necessary modules such as Face Anti-Spoofing are also analyzed. To the best of our knowledge, we are the first to tackle this issue directly. The data and code along with future updates to the models and hardware will be made available at https://github.com/AyanBiswas19/Resource_Efficient_FR.

Abstract

Biometric signal consisting of irrelevant or non-distinctive features can contain useful correlational properties that privacy-preserving verification schemes can exploit. While an efficient protocol for iris verification using noise has been presented, it is not applicable to other widely used modalities, i.e., face and fingerprint, since the methods of noise extraction and comparison are different. In this work, we design a verification protocol for secure dot product computation and also propose noise extraction mechanisms for face and fingerprint modalities. We evaluate the performance of the protocol on CFP, LFW, CelebA, FVC 2004 DB1A, DB2A, DB3A, and SOCOFing datasets. While the protocol exhibits a slight degradation in accuracy, it provides information-theoretic security with a practical computational complexity.

Abstract

For decades, fingerprint recognition has been prevalent for security, forensics, and other biometric applications. However, the availability of good-quality fingerprints is challenging, making recognition difficult. Fingerprint images might be degraded with a poor ridge structure and noisy or less contrasting backgrounds. Hence, fingerprint enhancement plays a vital role in the early stages of the fingerprint recognition/verification pipeline. In this paper, we investigate and improvise the encoder-decoder style architecture and suggest intuitive modifications to U-Net to enhance low-quality fingerprints effectively. We investigate the use of Discrete Wavelet Transform (DWT) for fingerprint enhancement and use a wavelet attention module instead of max pooling which proves advantageous for our task. Moreover, we replace regular convolutions with depthwise separable convolutions, which significantly reduces the memory footprint of the model without degrading the performance. We also demonstrate that incorporating domain knowledge with fingerprint minutiae prediction task can improve fingerprint reconstruction through multi-task learning. Furthermore, we also integrate the orientation estimation task to propagate the knowledge of ridge orientations to enhance the performance further. We present the experimental results and evaluate our model on FVC 2002 and NIST SD302 databases to show the effectiveness of our approach compared to previous works.

Abstract

A system for generating high-resolution de-warped omni-directional stereo image from captured omni-directional stereo image by correcting optical distortions using projection patterns is provided. The system includes a projection pattern capturing arrangement, a projector or a display, and a de-warping server. The projection pattern capturing arrangement includes one or more omnidirectional cameras to capture projection patterns from the captured omni-directional stereo image from each omni-directional stereo camera. The projector or the display displays the projection patterns. The de-warping server obtain the projection patterns and processes the projection patterns to generate high resolution de-warped omni-directional stereo image by correcting optical distortions in the captured omni-directional stereo image and mapping the captured omni-directional stereo image and the high resolution de-warped omni-directional stereo image.

Abstract

Evaluating the risk level of adversarial images is essential for safely deploying face authentication models in the real world. Popular approaches for physical-world attacks, such as print or replay attacks, suffer from some limitations, like including physical and geometrical artifacts. Recently adversarial attacks have gained attraction, which try to digitally deceive the learning strategy of a recognition system using slight modifications to the captured image. While most previous research assumes that the adversarial image could be digitally fed into the authentication systems, this is not always the case for systems deployed in the real world. This paper demonstrates the vulnerability of face authentication systems to adversarial images in physical world scenarios. We propose AdvGen, an automated Generative Adversarial Network, to simulate print and replay attacks and generate adversarial images that can fool state-of-the-art PADs in a physical domain attack setting. Using this attack strategy, the attack success rate goes up to 82.01%. We test AdvGen extensively on four datasets and ten state-of-the-art PADs. We also demonstrate the effectiveness of our attack by conducting experiments in a realistic, physical environment.

Abstract

Deep Learning on face recognition problems has shown extremely high accuracy owing to their ability in finding strongly discriminating features. However, face images in the wild show variations in pose, lighting, expressions, and the presence of facial attributes (for example eyeglasses). We ask, why then are these vari- ations not detected and used during the matching process? We demonstrate that this is indeed possible while restricting ourselves to facial attribute variation, to prove the case in point. We show two ways of doing so. a) By using the face attribute labels as a form of prior, we bin the matching template pairs into three bins depend- ing on whether each template of the matching pair possesses a given facial attribute or not. By operating on each bin and averaging the result, we better the EER of SOTA by over 1 % over a large set of matching pairs. b) We use the attribute labels and correlate them with each neuron of an embedding generated by a SOTA architecture pre-trained DNN on a large Face dataset and fine-tuned on face-attribute labels. We then suppress a set of maximally correlating neurons and perform matching after doing so. We demonstrate this improves the EER by over 2 %.

Abstract

3𝐷 fingerprint capture is less sensitive to skin moisture levels and avoids skin deformation, which is common in contact-based sensors, in addition to capturing depth information. Unfortunately, its adoption is limited due to high cost and system complexity. Photometric stereo provides an opportunity to build low-cost, simple sensors capable of high-quality 3𝐷 capture. However, it assumes that the surface being imaged is lambertian (unlike our fingers). We introduce the Split and Knit algorithm (SnK), a 3𝐷 reconstruction pipeline based on the photometric stereo for finger surfaces. It introduces an efficient way of estimating the direct illumination component, thus allowing us to do a higher-quality reconstruction of the entire finger surface. The algorithm also introduces a novel method to obtain the overall finger shape under NIR illumination, all using a single camera. Finally, we combine the overall finger shape and the ridge-valley point cloud to obtain a 3𝐷 finger phalange. The high-quality 3𝐷 reconstruction also results in better matching accuracy of the captured fingerprints. 1

Abstract

Video frame prediction is an interesting computer vision problem of predicting the future frames of a video sequence from a given set of context frames. Video prediction models have found wide-scale perspective applications in autonomous navigation, representation learning, and healthcare. However, predicting future frames is challenging due to the high dimensional and stochastic nature of video data. This work proposes a novel cycle consistency loss to disentangle video representation into a low dimensional time-dependent pose and time-independent content latent factors in two different VAE based video prediction models. The key motivation behind cycle consistency loss is that future frame predictions are more plausible and realistic if they reconstruct the previous frames. The proposed cycle consistency loss is also generic because it can be applied to other VAE-based stochastic video prediction architectures with slight architectural modifications. We validate our disentanglement hypothesis and the quality of long-range predictions on standard synthetic and challenging real-world datasets such as Stochastic Moving MNIST and BAIR.

Abstract

A system for generating high - resolution de - warped omni directional stereo image from captured omni - directional stereo image by correcting optical distortions using projec tion patterns is provided . The system includes a projection pattern capturing arrangement , a projector or a display , and a de - warping server . The projection pattern capturing arrangement includes one or more omnidirectional cameras to capture projection patterns from the captured omni directional stereo image from each omni - directional stereo camera . The projector or the display displays the projection patterns . The de - warping server obtain the projection pat terns and processes the projection patterns to generate high resolution de - warped omni - directional stereo image by cor recting optical distortions in the captured omni - directional stereo image and mapping the captured omni - directional stereo image and the high resolution de - warped omni directional stereo image

Abstract

Fingerprint feature extraction is a task that is solved using either a global or a local representation. State-of-the-art global approaches use heavy deep learning models to process the full fingerprint image at once, which makes the corresponding approach memory intensive. On the other hand, local approaches involve minutiae based patch extraction, multiple feature extraction steps and an expensive matching stage, which make the corresponding approach time intensive. However, both these approaches provide useful and sometimes exclusive insights for solving the problem. Using both approaches together for extracting fingerprint representations is semantically useful but quite inefficient. Our convolutional transformer based approach with an in-built minutiae extractor provides a time and memory efficient solution to extract a global as well as a local representation of the fingerprint. The use of these …