Kristen Allen | Carnegie Mellon University (original) (raw)
Papers by Kristen Allen
During the perinatal period, psychosocial health risks, including depression and intimate partner... more During the perinatal period, psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for parents and children. To appropriately intervene, healthcare professionals must first identify those at risk, yet stigma often prevents people from directly disclosing the information needed to prompt an assessment. We examine indirect methods of eliciting and analyzing information that could indicate psychosocial risks. Short diary entries by peripartum women exhibit thematic patterns, extracted by topic modeling, and emotional perspective, drawn from dictionary-informed sentiment features. Using these features, we use regularized regression to predict screening measures of depression and psychological aggression by an intimate partner. Journal text entries quantified through topic models and sentiment features show promise for depression prediction, with performance almost as good as closed-form questions. Text-based fe...
ArXiv, 2020
During the perinatal period, psychosocial health risks, including depression and intimate partner... more During the perinatal period, psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for parents and children. To appropriately intervene, healthcare professionals must first identify those at risk, yet stigma often prevents people from directly disclosing the information needed to prompt an assessment. We examine indirect methods of eliciting and analyzing information that could indicate psychosocial risks. Short diary entries by peripartum women exhibit thematic patterns, extracted by topic modeling, and emotional perspective, drawn from dictionary-informed sentiment features. Using these features, we use regularized regression to predict screening measures of depression and psychological aggression by an intimate partner. Journal text entries quantified through topic models and sentiment features show promise for depression prediction, with performance almost as good as closed-form questions. Text-based fe...
This work aims to infer mental health status from public text for early detection of suicide risk... more This work aims to infer mental health status from public text for early detection of suicide risk. It contributes to Shared Task A in the 2019 CLPsych workshop by predicting users’ suicide risk given posts in the Reddit subforum r/SuicideWatch. We use a convolutional neural network to incorporate LIWC information at the Reddit post level about topics discussed, first-person focus, emotional experience, grammatical choices, and thematic style. In sorting users into one of four risk categories, our best system’s macro-averaged F1 score was 0.50 on the withheld test set. The work demonstrates the predictive power of the Linguistic Inquiry and Word Count dictionary, in conjunction with a convolutional network and holistic consideration of each post and user.
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016
Automatic face recognition technologies have seen significant improvements in performance due to ... more Automatic face recognition technologies have seen significant improvements in performance due to a combination of advances in deep learning and availability of larger datasets for training deep networks. Since recognizing faces is a task that humans are believed to be very good at, it is only natural to compare the relative performance of automated face recognition and humans when processing fully unconstrained facial imagery. In this work, we expand on previous studies of the recognition accuracy of humans and automated systems by performing several novel analyses utilizing unconstrained face imagery. We examine the impact on performance when human recognizers are presented with varying amounts of imagery per subject, immutable attributes such as gender, and circumstantial attributes such as occlusion, illumination, and pose. Results indicate that humans greatly outperform state of the art automated face recognition algorithms on the challenging IJB-A dataset.
2015 International Conference on Biometrics (ICB), 2015
As unconstrained face recognition datasets progress from containing faces that can be automatical... more As unconstrained face recognition datasets progress from containing faces that can be automatically detected by commodity face detectors to face imagery with full pose variations that must instead be manually localized, a significant amount of annotation effort is required for developing benchmark datasets. In this work we describe a systematic approach for annotating fully unconstrained face imagery using crowdsourced labor. For such data preparation, a cascade of crowdsourced tasks are performed, which begins with bounding box annotations on all faces contained in images and videos, followed by identification of the labelled person of interest in such imagery, and, finally, landmark annotation of key facial fiducial points. In order to allow such annotations to scale to large volumes of imagery, a software system architecture is provided which achieves a sustained rate of 30,000 annotations per hour (or 500 manual annotations per minute). While previous crowdsourcing guidance described in the literature generally involved multiple choice questions or text input, our tasks required annotators to provide geometric primitives (rectangles and points) in images. As such, algorithms are provided for combining multiple annotations of an image into a single result, and automatically measuring the quality of a given annotation. Finally, other guidance is provided for improving the accuracy and scalability of crowdsourced image annotation for face detection and recognition.
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016
This study focuses on the problem of extracting consistent and accurate face bounding box annotat... more This study focuses on the problem of extracting consistent and accurate face bounding box annotations from crowdsourced workers. Aiming to provide benchmark datasets for facial recognition training and testing, we create a 'gold standard' set against which consolidated face bounding box annotations can be evaluated. An evaluation methodology based on scores for several features of bounding box annotations is presented and is shown to predict consolidation performance using information gathered from crowdsourced annotations. Based on this foundation, we present "Grouper," a method leveraging density-based clustering to consolidate annotations by crowd workers. We demonstrate that the proposed consolidation scheme, which should be extensible to any number of region annotation consolidations, improves upon metadata released with the IARPA Janus Benchmark-A. Finally, we compare FR performance using the originally provided IJB-A annotations and Grouper and determine that similarity to the gold standard as measured by our evaluation metric does predict recognition performance.
This study focuses on the problem of extracting consistent and accurate face bounding box annotat... more This study focuses on the problem of extracting consistent and accurate face bounding box annotations from crowdsourced workers. Aiming to provide benchmark datasets for facial recognition training and testing, we create a ‘gold standard’ set against which consolidated face bounding box annotations can be evaluated. An evaluation methodology based on scores for several features of bounding box annotations is presented and is shown to predict consolidation performance using information gathered from crowdsourced annotations. Based on this foundation, we present “Grouper,” a method leveraging density-based clustering to consolidate annotations by crowd workers. We demonstrate that the proposed consolidation scheme, which should be extensible to any number of region annotation consolidations, improves upon metadata released with the IARPA Janus Benchmark-A. Finally, we compare FR performance using the originally provided IJB-A annotations and Grouper and determine that similarity to the gold standard as measured by our evaluation metric does predict recognition performance.
Automatic face recognition technologies have seen significant improvements in performance due to ... more Automatic face recognition technologies have seen significant improvements in performance due to a combination of advances in deep learning and availability of larger datasets for training deep networks. Since recognizing faces is a task humans are believed to excel at, we use human performance on fully unconstrained facial imagery as a baseline against which to compare the relative performance of automated face recognition. In this work, we expand on previous studies of the recognition accuracy of humans and automated systems by performing several novel analyses utilizing unconstrained face imagery. We examine the impact on performance when human recognizers are presented with varying amounts of imagery per subject, immutable attributes such as gender, and circumstantial attributes such as occlusion, illumination, and pose. Results indicate that humans greatly outperform state-of-the-art automated face recognition algorithms on the challenging IJB-A dataset.
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accu... more Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.
As unconstrained face recognition datasets progress from containing faces that can be automatical... more As unconstrained face recognition datasets progress from containing faces that can be automatically detected by commodity face detectors to face imagery with full pose variations that must instead be manually localized, a significant amount of annotation effort is required for developing benchmark datasets. In this work we describe a systematic approach for annotating fully unconstrained face imagery using crowdsourced labor. For such data preparation, a cascade of crowdsourced tasks are performed, which begins with bounding box annotations on all faces contained in images and videos, followed by identification of the labelled person of interest in such imagery, and, finally, landmark annotation of key facial fiducial points. In order to allow such annotations to scale to large volumes of imagery, a software system architecture is provided which achieves a sustained rate of 30,000 annotations per hour (or 500 manual annotations per minute). While previous crowdsourcing guidance described in the literature generally involved multiple choice questions or text input, our tasks required annotators to provide geometric primitives (rectangles and points) in images. As such, algorithms are provided for combining multiple annotations of an image into a single result, and automatically measuring the quality of a given annotation. Finally, other guidance is provided for improving the accuracy and scalability of crowdsourced image annotation for face detection and recognition.
During the perinatal period, psychosocial health risks, including depression and intimate partner... more During the perinatal period, psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for parents and children. To appropriately intervene, healthcare professionals must first identify those at risk, yet stigma often prevents people from directly disclosing the information needed to prompt an assessment. We examine indirect methods of eliciting and analyzing information that could indicate psychosocial risks. Short diary entries by peripartum women exhibit thematic patterns, extracted by topic modeling, and emotional perspective, drawn from dictionary-informed sentiment features. Using these features, we use regularized regression to predict screening measures of depression and psychological aggression by an intimate partner. Journal text entries quantified through topic models and sentiment features show promise for depression prediction, with performance almost as good as closed-form questions. Text-based fe...
ArXiv, 2020
During the perinatal period, psychosocial health risks, including depression and intimate partner... more During the perinatal period, psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for parents and children. To appropriately intervene, healthcare professionals must first identify those at risk, yet stigma often prevents people from directly disclosing the information needed to prompt an assessment. We examine indirect methods of eliciting and analyzing information that could indicate psychosocial risks. Short diary entries by peripartum women exhibit thematic patterns, extracted by topic modeling, and emotional perspective, drawn from dictionary-informed sentiment features. Using these features, we use regularized regression to predict screening measures of depression and psychological aggression by an intimate partner. Journal text entries quantified through topic models and sentiment features show promise for depression prediction, with performance almost as good as closed-form questions. Text-based fe...
This work aims to infer mental health status from public text for early detection of suicide risk... more This work aims to infer mental health status from public text for early detection of suicide risk. It contributes to Shared Task A in the 2019 CLPsych workshop by predicting users’ suicide risk given posts in the Reddit subforum r/SuicideWatch. We use a convolutional neural network to incorporate LIWC information at the Reddit post level about topics discussed, first-person focus, emotional experience, grammatical choices, and thematic style. In sorting users into one of four risk categories, our best system’s macro-averaged F1 score was 0.50 on the withheld test set. The work demonstrates the predictive power of the Linguistic Inquiry and Word Count dictionary, in conjunction with a convolutional network and holistic consideration of each post and user.
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016
Automatic face recognition technologies have seen significant improvements in performance due to ... more Automatic face recognition technologies have seen significant improvements in performance due to a combination of advances in deep learning and availability of larger datasets for training deep networks. Since recognizing faces is a task that humans are believed to be very good at, it is only natural to compare the relative performance of automated face recognition and humans when processing fully unconstrained facial imagery. In this work, we expand on previous studies of the recognition accuracy of humans and automated systems by performing several novel analyses utilizing unconstrained face imagery. We examine the impact on performance when human recognizers are presented with varying amounts of imagery per subject, immutable attributes such as gender, and circumstantial attributes such as occlusion, illumination, and pose. Results indicate that humans greatly outperform state of the art automated face recognition algorithms on the challenging IJB-A dataset.
2015 International Conference on Biometrics (ICB), 2015
As unconstrained face recognition datasets progress from containing faces that can be automatical... more As unconstrained face recognition datasets progress from containing faces that can be automatically detected by commodity face detectors to face imagery with full pose variations that must instead be manually localized, a significant amount of annotation effort is required for developing benchmark datasets. In this work we describe a systematic approach for annotating fully unconstrained face imagery using crowdsourced labor. For such data preparation, a cascade of crowdsourced tasks are performed, which begins with bounding box annotations on all faces contained in images and videos, followed by identification of the labelled person of interest in such imagery, and, finally, landmark annotation of key facial fiducial points. In order to allow such annotations to scale to large volumes of imagery, a software system architecture is provided which achieves a sustained rate of 30,000 annotations per hour (or 500 manual annotations per minute). While previous crowdsourcing guidance described in the literature generally involved multiple choice questions or text input, our tasks required annotators to provide geometric primitives (rectangles and points) in images. As such, algorithms are provided for combining multiple annotations of an image into a single result, and automatically measuring the quality of a given annotation. Finally, other guidance is provided for improving the accuracy and scalability of crowdsourced image annotation for face detection and recognition.
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016
This study focuses on the problem of extracting consistent and accurate face bounding box annotat... more This study focuses on the problem of extracting consistent and accurate face bounding box annotations from crowdsourced workers. Aiming to provide benchmark datasets for facial recognition training and testing, we create a 'gold standard' set against which consolidated face bounding box annotations can be evaluated. An evaluation methodology based on scores for several features of bounding box annotations is presented and is shown to predict consolidation performance using information gathered from crowdsourced annotations. Based on this foundation, we present "Grouper," a method leveraging density-based clustering to consolidate annotations by crowd workers. We demonstrate that the proposed consolidation scheme, which should be extensible to any number of region annotation consolidations, improves upon metadata released with the IARPA Janus Benchmark-A. Finally, we compare FR performance using the originally provided IJB-A annotations and Grouper and determine that similarity to the gold standard as measured by our evaluation metric does predict recognition performance.
This study focuses on the problem of extracting consistent and accurate face bounding box annotat... more This study focuses on the problem of extracting consistent and accurate face bounding box annotations from crowdsourced workers. Aiming to provide benchmark datasets for facial recognition training and testing, we create a ‘gold standard’ set against which consolidated face bounding box annotations can be evaluated. An evaluation methodology based on scores for several features of bounding box annotations is presented and is shown to predict consolidation performance using information gathered from crowdsourced annotations. Based on this foundation, we present “Grouper,” a method leveraging density-based clustering to consolidate annotations by crowd workers. We demonstrate that the proposed consolidation scheme, which should be extensible to any number of region annotation consolidations, improves upon metadata released with the IARPA Janus Benchmark-A. Finally, we compare FR performance using the originally provided IJB-A annotations and Grouper and determine that similarity to the gold standard as measured by our evaluation metric does predict recognition performance.
Automatic face recognition technologies have seen significant improvements in performance due to ... more Automatic face recognition technologies have seen significant improvements in performance due to a combination of advances in deep learning and availability of larger datasets for training deep networks. Since recognizing faces is a task humans are believed to excel at, we use human performance on fully unconstrained facial imagery as a baseline against which to compare the relative performance of automated face recognition. In this work, we expand on previous studies of the recognition accuracy of humans and automated systems by performing several novel analyses utilizing unconstrained face imagery. We examine the impact on performance when human recognizers are presented with varying amounts of imagery per subject, immutable attributes such as gender, and circumstantial attributes such as occlusion, illumination, and pose. Results indicate that humans greatly outperform state-of-the-art automated face recognition algorithms on the challenging IJB-A dataset.
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accu... more Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.
As unconstrained face recognition datasets progress from containing faces that can be automatical... more As unconstrained face recognition datasets progress from containing faces that can be automatically detected by commodity face detectors to face imagery with full pose variations that must instead be manually localized, a significant amount of annotation effort is required for developing benchmark datasets. In this work we describe a systematic approach for annotating fully unconstrained face imagery using crowdsourced labor. For such data preparation, a cascade of crowdsourced tasks are performed, which begins with bounding box annotations on all faces contained in images and videos, followed by identification of the labelled person of interest in such imagery, and, finally, landmark annotation of key facial fiducial points. In order to allow such annotations to scale to large volumes of imagery, a software system architecture is provided which achieves a sustained rate of 30,000 annotations per hour (or 500 manual annotations per minute). While previous crowdsourcing guidance described in the literature generally involved multiple choice questions or text input, our tasks required annotators to provide geometric primitives (rectangles and points) in images. As such, algorithms are provided for combining multiple annotations of an image into a single result, and automatically measuring the quality of a given annotation. Finally, other guidance is provided for improving the accuracy and scalability of crowdsourced image annotation for face detection and recognition.