Imparting Soft-Biometric Privacy to Face Images
Face images are widely used in biometric recognition systems. Yet, a face image can disclose some auxiliary information (age, gender, race, ..) about the individual beyond recognition. Extracting such auxiliary information is considered a privacy violation, and our goal is to confound such information.
Speaker recognition from degraded audio samples
In this project, we are working on developing deep learning-based algorithms for extracting robust speaker dependent voice characteristics from raw audio data and audio features, such as Mel-Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding (LPC), for recognizing speakers in degraded audio samples.
Fusing Face and Voice Modalities for Improved Biometric Recognition
In this project, we are developing algorithms for improving biometric recognition in videos by fusing face and voice modalities.
Presentation attack detection in biometrics
Attempting to fool a biometric system can take many forms, in this work we train a machine to recognize these attempts to further the security of these systems.
Graphical Models for Predicting Missing or Incomplete Biographic Data in Biometric Records
We create a network of biometric records containing information about person such as a face image, name, gender, or ethnicity. Some records in the graph have missing information while some records are correct. We use the graph structure to infer missing information from the correct records.
Relativistic Discriminator: A One-Class Classifier for Generalized Iris Presentation Attack Detection
Presentation attacks are a vulnerability of iris based recognition systems. In this work, we present a discriminator based approach to detecting these presentation attacks.
Attribute prediction from near-infrared ocular images
Soft biometrics, such as race, age, and eye color, provide interesting demographic information as well as a high level description of an individual. In this work, we try to gather those metrics from Near Infrared (NIR) iris images.
Exploring the Use of IrisCodes for Presentation Attack Detection
In iris recognition, the IrisCode is the commonly used representation for the iris data used to recognize an individual. In this work we investigate if these IrisCodes can be used to detect a presentation attack.
Matching Thermal to Visible Face Images Using a Semantic-Guided Generative Adversarial Network
Face recognition in spectra of light other than visible presents a unique challenge as well as great benefits. In this work we use a semantic-guided generative adversarial network (SG-GAN) to match infrared (colloquially, thermal) images to visible spectrum images.
Camera sensor identification for biometric images
Each camera sensor can be uniquely identified using the sensor noise pattern present in the images. We use the principle of Photo Response Non-Uniformity (PRNU) which captures this sensor noise pattern to identify iris sensors and also study the impact of different photometric transformations on sensor identification. Furthermore, we investigate whether the PRNU based sensor classifier can be fooled into misclassifying an image to an incorrect sensor.
DeepTalk: Vocal Style Encoding for Speaker Recognition and Speech Synthesis
In this project, we are developing deep learning-based algorithms for extracting speaker-dependent behavioral speech characteristics for encoding a given speaker's vocal style. We further use the extracted behavioral speech characteristics for improving state-of-the-art speaker recognition and speech synthesis performance.
DeepVOX: Discovering Features from Raw Audio for Speaker Recognition in Non-ideal Audio Signals
In this project, we learn a deep learning-based speech filterbank, called DeepVOX, directly from raw speech audio for extracting robust speaker-dependent voice characteristics. DeepVOX is demonstrated to outperform traditional handcrafted speech features, such as MFCC and LPC, in the context of speaker verification in non-ideal audio signals.
Conditional Identity Disentanglement for Differential Face Morph Detection
Face morphing strategically mixes two or more face images such that the composite image matches successfully to all constituents in terms of biometric utility. Therefore, a morphed face image can be utilized adversarially in an identity document and poses a security threat. We propose a conditional identity disentanglement network using a GAN that not only performs differential face morph detection guided by a trusted non-morphed reference image but also recovers the second identity used in creating the morph.
One-Shot Representational Learning for Joint Biometric and Device Authentication
Smartphones often employ multi-factor authentication to ensure the authorized user is accessing a remote application, such as online banking using a registered device. It requires simultaneous biometric and device authentication. We propose an end-to-end approach to learn a joint representation that encapsulates biometric and sensor (device) identities from a single biometric image.
Image-level Iris Morph Attack
In this project, we explore morph attacks as a security threat to iris recognition systems. The attack associates a single morphed image to two or more different identities, violating the fundamental uniqueness property of biometric systems.
We develop a specialty lookalike disambiguator used in conjunction with a general-purpose face matcher to improve face identification. The disambiguator network is trained on lookalike face images, i.e., those faces which a general-purpose face matcher struggles to correctly distinguish.