The construction and validation of a series of color film loops to illustrate selected women's lacrosse techniques recommended for beginners

The purpose of this study was to construct and validate a series of color film loops to illustrate selected women's lacrosse techniques for beginners. The content to be included in the film loop series was based on a careful search of the lacrosse literature and the responses to a questionnaire by well known experts in the field. The film loop series consisted of ten techniques: THE GRIP-CRADLE, PIVOTING, CATCHING, THE OVERARM PASS, THE UNDERARM PASS, PICKING-UP: BALL STATIONARY - BALL ROLLING AWAY FROM PLAYER, PICKING-UP: BALL ROLLING TOWARD PLAYER, DODGING, BODY CHECKING, and CROSSE CHECKING. Each film loop consisted of the title, followed by execution of the technique three times at 24 frames per second and two times at 70 frames per second for each angle viewed. Two members of the 1973 United States Women's Lacrosse Team served as performers of the techniques included in the film loop series

Deep Learning for Face Anti-Spoofing: A Survey

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, traditional FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

FedBiometric: Image Features Based Biometric Presentation Attack Detection Using Hybrid CNNs-SVM in Federated Learning

In the past few years, biometric identification systems have become popular for personal, national, and global security. In addition to other biometric modalities, facial and fingerprint recognition have gained popularity due to their uniqueness, stability, convenience, and cost-effectiveness compared to other biometric modalities. However, the evolution of fake biometrics, such as printed materials, 2D or 3D faces, makeup, and cosmetics, has brought new challenges. As a result of these modifications, several facial and fingerprint Presentation Attack Detection methods have been proposed to distinguish between live and spoof faces or fingerprints. Federated learning can play a significant role in this problem due to its distributed learning setting and privacy-preserving advantages. This work proposes a hybrid ResNet50-SVM based federated learning model for facial Presentation Attack Detection utilizing Local Binary Pattern (LBP), or Gabor filter-based extracted image features. For fingerprint Presentation Attack Detection (PAD), this work proposes a hybrid CNN-SVM based federated learning model utilizing Local Binary Pattern (LBP), or Histograms of Oriented Gradient (HOG)-based extracted image features

IMPULSE moment-by-moment test:An implicit measure of affective responses to audiovisual televised or digital advertisements

IMPULSE is a novel method for detecting affective responses to dynamic audiovisual content. It is an implicit reaction time test that is carried out while an audiovisual clip (e.g., a television commercial) plays in the background and measures feelings that are congruent or incongruent with the content of the clip. The results of three experiments illustrate the following four advantages of IMPULSE over self-reported and biometric methods: (1) being less susceptible to typical confounds associated with explicit measures, (2) being easier to measure deep-seated and often nonconscious emotions, (3) being better able to detect a broad range of emotions and feelings, and (4) being more efficient to implement as an online method.Published versio

Rail Passenger Selective Screening Summit, MTI S-09-01

This publication is an edited transcript of the Rail Passenger Selective Screening Summit, which was co-sponsored by MTI and the American Public Transportation Association (APTA) in Chicago, Illinois on June 18, 2009, during APTA´s annual Rail Conference. The workshop was moderated by Brian Michael Jenkins, director, Mineta Transportation Institute\u27s National Transportation Security Center of Excellence (NTSCOE). Speakers included Bruce R. Butterworth, co-author, Selective Screening of Rail Passengers; Greg Hull, president, American Public Transportation Association (APTA); Paul MacMillan, chief of police, Massachusetts Bay Transportation Authority, Transit Police Department; Ron Masciana, deputy chief, Metropolitan Transit Authority (MTA), New York; Jesus Ojeda, security coordinator, Southern California Regional Rail Authority; Ed Phillips, operations deputy, Office of Security, Amtrak; and John P. Sammon, assistant administrator, Transportation Sector Network Management, Transportation Security Administration (TSA