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Forecasting methods and applications.

児童の権利に関する条約 = Convention on the rights of the child

Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g., access control) and noncooperative (e.g., surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioral characteristics of human beings, which enable for individuals to be reliably recognized using automated algorithms. Recently, however, there has been a wave of public and academic concerns regarding the existence of systemic bias in automated decision systems (including biometrics). Most prominently, face recognition algorithms have often been labeled as “racist” or “biased” by the media, nongovernmental organizations, and researchers alike. The main contributions of this article are: 1) an overview of the topic of algorithmic bias in the context of biometrics; 2) a comprehensive survey of the existing literature on biometric bias estimation and mitigation; 3) a discussion of the pertinent technical and social matters; and 4) an outline of the remaining challenges and future work items, both from technological and social points of view.

Demographic Bias in Biometrics: A Survey on an Emerging Challenge

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.

MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.

Handwritten text recognition needs some preprocessing steps for better recognition. One of the preprocessing task is handwritten text document binarization. Several binarization approaches are proposed previously and several are widely used in handwritten text binarization but the choice of most appropriate binarization approach for handwritten document is a very difficult itself. In this paper, we propose an adaptive binarization approach which can handle both continuous and abrupt intensity variations across the lines as well as words for handwritten or printed document. Experiments found that, the proposed approach is very simple as it uses only addition operations with constant complexity, and it also gives competitive results for handwritten (printed) documents compared to standard binarization approaches.

A simple and novel adaptive binarization approach for handwritten documents

Various activities in everyday life require us to verify our identity by demonstrating our ID document containing face images, for example, voter ID, passports, driver licence, to human administrators. However, this procedure is reluctant, unreliable and labor comprehensive. An automatic framework for verifying ID record photographs to live face pictures (selfies) progressively and with high precision is required. Cross-domain biometrics is another requirement, which represents se-veral additional challenges, including harsh illumination conditions, pose variations, noise, among others. In this paper, we propose an algorithm to meet this objective. We first extract faces from ID document and selfie using Multi-task Cascaded Convolutional Networks. To extract prominent features from the data, we apply a VGG face model which is a CNN-based transfer learning approach. Finally, we validate the methods using a novel FaceId-Selfie dataset comprising 600 individuals using cosine distance measure. Results show that 74% accuracy is achieved on FaceId-Selfie dataset.

FaceID: Verification of Face in Selfie and ID Document

Traditional video analysis methods can generate summary of day's long videos. While generating synopsis video maintaining the motion structure of important activities present in a video sequence is of great concern in research communities and industry. In this paper, we present an automatic and scalable approach for automatic detection of important activities in a video based on different spatiotemporal criteria used as a signature. To maintain the important context cues we propose an online motion structure preserved synopsis approach, which can retain the behavior interactions between different objects in an original video while condensing as much content as possible. A hierarchical fashion is employed to efficiently search important activities present in a video sequence, and generating synopsis video of those important activities in which both the spatial collision and the temporal consistency are considered. Experimental results on numerous (six) video sequences demonstrate the promise of the proposed approach.

Generalised Spatio Temporal Feature Based Important Activity Synopsis Generation

In video surveillance, person re-identification (re-id) is a popular technique to automatically finding whether a person has been already seen in a group of cameras. In the recent years, availability of large-scale datasets, the deep learning-based approaches have made significant improvement in the accuracy over the years as compared to hand-crafted approaches. In this paper, we have distinguished the person re-id approaches into two categories, i.e., image-based and video-based approaches; deep learning approaches are reviewed in both categories. This paper contains the brief survey of deep learning approaches on both image and video person re-id datasets. We have also presented the current ongoing works, issues, and future directions in large-scale datasets.

Deep Convolutional Neural Network for Person Re-identification: A Comprehensive Review

Lip segmentation is an essential stage in many multimedia systems such as video -conferencing, lip reading, or low bit rate coding communication systems. It is also useful in various image / video acquisition devices encouraged the development of many computer vision applications, such as vision -based surveillance, vision based man machine interfaces, vision-based biometrics, and so on. Hence extraction of lip becomes a broad field. This paper presents an algorithm for extraction of lip contour. We have used two algorithms for this. Our first algorithm is a color based algorithm derived from a quadratic polynomial, with the assumption that color of lips should lie in the range of blue. The accuracy of the first proposed algorithm is 91% in case of normal skin people, and 95% in case of fairer skin color people. Our second algorithm is model a based algorithm which gives accuracy up to 9 6%. Keyword: Lip contour detection, Lip segmentation, Lip extraction from face.

http://inpressco.com/wp-content/uploads/2012/06/Paper7265-2691.pdf

Neeta Nain

Papers

A simple and novel adaptive binarization approach for handwritten documents

FaceID: Verification of Face in Selfie and ID Document

Generalised Spatio Temporal Feature Based Important Activity Synopsis Generation

Deep Convolutional Neural Network for Person Re-identification: A Comprehensive Review

Extraction of Lip Contour from Face