Nec Corporation

Michigan State University

Ford Global Technolgoies, LLC

Nec C Orporation

Xi Yin

Manmohan Chandraker

Xiang Yu

Kihyuk Sohn

Xiaoming Liu

Kwaku O Prakah-Asante

Jian Wan

Ziyuan Zhang

Yousef Atoum

Mike Blommer

Luan Tran

Luan Quoc Tran

A system and method for identifying a subject using imaging are provided. In some aspects, the method includes receiving an image depicting a subject to be identified, and applying a trained Disentangled Representation learning-Generative Adversarial Network (DR-GAN) to the image to generate an identity representation of the subject, wherein the DR-GAN comprises a discriminator and a generator having at least one of an encoder and a decoder. The method also includes identifying the subject using the identity representation, and generating a report indicative of the subject identified.

Disentangled representation learning generative adversarial network for pose-invariant face recognition

Gait, the walking pattern of individuals, is one of the most important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as the gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and view angle. To remedy this issue, a novel AutoEncoder framework is presented to explicitly disentangle pose and appearance features from RGB imagery and a long short-term memory integration of pose features over time produces the gait feature.

Systems and methods for gait recognition via disentangled representation learning

A computer-implemented method, system, and computer program product are provided for facial recognition. The method includes receiving, by a processor device, a plurality of images. The method also includes extracting, by the processor device with a feature extractor utilizing a convolutional neural network (CNN) with an enlarged intra-class variance of long-tail classes, feature vectors for each of the plurality of images. The method additionally includes generating, by the processor device with a feature generator, discriminative feature vectors for each of the feature vectors. The method further includes classifying, by the processor device utilizing a fully connected classifier, an identity from the discriminative feature vector. The method also includes control an operation of a processor-based machine to react in accordance with the identity.

Long-tail large scale face recognition by non-linear feature level domain adaptation

Long-tail large scale face recognition by non-linear feature level domain adaption

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Xi Yin