Abstract: A system for improved personal authentication using a combination of pictures and text passwords that gets more secure over time as more users utilize the system. The system increases the security of authentication by using methods that are easier for a valid user to remember (by using cues), while simultaneously more difficult for an attacker to guess, even in the case where that attacker might be looking over the user's shoulder. A user will initially register an image and a question/answer pair about this image when setting up an account. At authentication time, the user will be presented with a set of images (one of which may be the user's registered image) and question (which serves as a memory cue to the user) and will be asked to provide the registered answer. By selecting the correct image and providing the correct answer to the given question, the user can prove their identity.

Abstract: Time lapse, characteristic of aging, is a complex process that affects the reliability and security of biometric face recognition systems. Systems and methods use deep learning, in general, and convolutional neural networks (CNN), in particular, for automatic rather than hand-crafted feature extraction for robust face recognition across time lapse. A CNN architecture using the VGG-Face deep (neural network) learning produces highly discriminative and interoperable features that are robust to aging variations even across a mix of biometric datasets. The features extracted show high inter-class and low intra-class variability leading to low generalization errors on aging datasets using ensembles of subspace discriminant classifiers.

Abstract: Time lapse, characteristic of aging, is a complex process that affects the reliability and security of biometric face recognition systems. Systems and methods use deep learning, in general, and convolutional neural networks (CNN), in particular, for automatic rather than hand-crafted feature extraction for robust face recognition across time lapse. A CNN architecture using the VGG-Face deep (neural network) learning produces highly discriminative and interoperable features that are robust to aging variations even across a mix of biometric datasets. The features extracted show high inter-class and low intra-class variability leading to low generalization errors on aging datasets using ensembles of subspace discriminant classifiers.

Abstract: Systems, methods, and/or techniques for performing active authentication on a device during a session with a user may be provided to detect an imposter. To perform active authentication, meta-recognition may be performed. For example, an ensemble method to facilitate detection of the imposter. The ensemble method may user discrimination using random boost and/or intrusion or change detection using transduction. Scores and/or results may be received from the ensemble method. A determination may be made, based on the scores and/or results, whether to continue to enable access to the device, whether to invoke collaborative filtering and/or challenge-responses for additional information, and/or whether to lock the device. Based on the determination, user profile adaptation on a user profile used in the ensemble method and/or the determination and/or retrain the ensemble method, collaborative filtering and/or challenge-responses, and/or a lock procedure may be performed.

Abstract: A new robust human authentication system, device, and instructions, embeddable in a physical and tangible computer readable medium, for determining if at least one test image obtained using an imaging device matches at least one training image in an enrollment database, are disclosed. This invention applies the concepts of appearance (PCA or PCA+LDA) and holistic anthropometrics that include head, face, neck, and shoulder linear and non-linear geometric measurements. The appearance (“eigen”) coefficients and holistic anthropometric measurements selected may be used as feature vectors. A boosting algorithm ranks features as “weak learners” and combines their outputs for “strong” recognition.

Abstract: A robust recognition-by-parts authentication system for comparing and authenticating a test image with at least one training image is disclosed. This invention applies the concepts of recognition-by-parts, boosting, and transduction.

Abstract: A data stream change detector including a receiving module, a preprocessor module, a clustering module, a strangeness module, a p-value module, a martingale value determination module, comparison module, and an output module. The receiving module accepts new data vectors that originate from a sequence of data in a data stream. Preprocessor module preprocesses the new data vector using a filter. The clustering module clusters the new data vector with previously received data vectors. Strangeness module computes a strangeness value for each of the previously received data vectors. The p-value module calculates a p-value for the new data vector using the strangeness value. Martingale value determination module calculates a martingale value for the new data vector using the p-value. Comparison module compares the martingale value with a threshold value; and sets an indicator if a change if the martingale is greater than the threshold.

Abstract: A recognition-by-parts authentication system for determining if a physical test target represented in test image(s) obtained using an imaging device matches a physical training target represented in training image(s). The system includes a multitude of adaptive and robust correlation filters. Each of the adaptive and robust correlation filters is configured to generate correlation-peak-strength and distance-from-origin data using a multitude of related images. Each of the multitude of related images representing a similar part of a larger image. The related images originate from the test image(s) and training image(s).

Abstract: A new robust human authentication system, device, and instructions, embeddable in a physical and tangible computer readable medium, for determining if at least one test image obtained using an imaging device matches at least one training image in an enrollment database, are disclosed. This invention applies the concepts of appearance (PCA or PCA+LDA) and holistic anthropometrics that include head, face, neck, and shoulder linear and non-linear geometric measurements. The appearance (“eigen”) coefficients and holistic anthropometric measurements selected may be used as feature vectors. A boosting algorithm ranks features as “weak learners” and combines their outputs for “strong” recognition.

Abstract: A robust recognition-by-parts authentication system for comparing and authenticating a test image with at least one training image is disclosed. This invention applies the concepts of recognition-by-parts, boosting, and transduction.

Abstract: A system and method for determining whether at least one data point is interesting may be provided. The system may include, among other things, a memory for the at least one data point and a query-by-transduction module configured to assign a plurality of labels to the at least one data point, wherein each label among the plurality of labels corresponds to a respective classification for the at least one data point and wherein each label corresponds to a respective confidence metric that indicates a level of confidence that the respectively corresponding label accurately classifies the at least one data point, analyze the plurality of confidence metrics, and determine whether the at least one data point is interesting based on the analysis.

Abstract: An open set recognition system utilizing transductive inference including capture device(s), a basis, quality checker(s), feature extractor(s), a gallery, a rejection threshold, a storage mechanism, and a recognition stage. The basis encodes sample(s) and is derived using training samples. The feature extractor(s) generates signature(s) from sample(s) using the basis. The rejection threshold is created using a rejection threshold learning mechanism that calculates the rejection threshold using sample(s) by: swapping a sample identifier with other sample identifier(s); computing a credibility value for the swapped sample identifiers; deriving a peak-to-side ratio distribution using the credibility values; and determining the rejection threshold using the peak-to-side ratio distribution. The open set recognition stage authenticates or reject as unknown the identity of unknown sample(s) using derived credibility values, derived peak-to-side ratios for the unknown sample and the rejection threshold.

Abstract: Disclosed is an open set recognition system that utilizes transductive inference. One embodiment of this open set recognition system includes capture device(s), a basis, quality checker(s), feature extractor(s), a gallery, a rejection threshold, a storage mechanism, and a recognition stage. The basis is configured to encode the sample(s) and is derived using representative training samples. The feature extractor(s) generates signature(s) from sample(s) using the basis. The rejection threshold may be created using a rejection threshold learning mechanism configured to calculate the rejection threshold using the sample(s) by: swapping one of the sample identifier with other possible sample identifier(s); computing a credibility value (p) for each of the swapped sample identifiers; deriving a peak-to-side ratio distribution using a multitude of the credibility values; and determining the rejection threshold using the peak-to-side ratio distribution.

Abstract: A method and system for determining the similarity between an image and at least one training sample is disclosed. This invention is a novel Gabor Feature Classifier (GFC), a principal application of which may be for face recognition. The GFC applies the Enhanced FLD Model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet transformation of images.

Abstract: A method for fractal image compression using reinforced learning is disclosed. The reinforced learning algorithm improves the performance of fractal image compression algorithms by improving image partitioning and transform family selection while considering the impact of domain to range matching. The present invention differs from other fractal image compression algorithms in that it makes decisions about transform family and image partitioning by generalizing from experience compressing small portions of an image to compressing an image as a whole.

Abstract: A method and system for determining the similarity between an image and at least one training sample is disclosed. This invention is a novel Gabor Feature Classifier (GFC), a principal application of which may be for face recognition. The GFC applies the Enhanced FLD Model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet transformation of images.