Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition

Wong, Yongkang; Chen, Shaokang; Mau, Sandra; Sanderson, Conrad; Lovell, Brian C.

doi:10.1109/cvprw.2011.5981881

Cited by 264 publications

(201 citation statements)

References 35 publications

Supporting

Mentioning

200

Contrasting

Unclassified

Order By: Relevance

“…To compare the performance of the 3 trackers, 18 video sequences -9 from entering and 9 from leaving (see Table 1) -are used from the Chokepoint dataset [15]. Each sequence views 5 individuals walking through the portal, one at a time.…”

Section: Methodsmentioning

confidence: 99%

A Comparison of Adaptive Appearance Methods for Tracking Faces in Video Surveillance

Dewan

Granger

Roli

et al. 2013

5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013)

View full text Add to dashboard Cite

Face recognition is increasingly employed by public safety organizations in decision support systems for video surveillance, to detect the presence of individuals of interest. In the context of spatiotemporal face recognition, tracking is an important function used to locate, follow and regroup faces of different individuals in a scene. Techniques for face tracking in video surveillance should be robust to changes in pose, expression and illumination, as well as occlusion in cluttered scenes. Given these challenges, trackers based on adaptive appearance modelling (AAM) typically improve target's state estimation because they initiate and update an internal face model per individual according to changes in facial appearance. In this paper, the performance of three AAM trackers -Incremental Visual Tracking (IVT), Tracking Learning Detection (TLD) and Discriminative Sparse Coding based Tracking (DSCT) -are compared for face tracking with video surveillance applications in mind. These methods are evaluated according to area overlap error, tracking error and time complexity using Chokepoint videos collected in uncontrolled video-surveillance environments, where individuals walk through portals. Results indicate that IVT outperforms the others in its ability to accurately track faces in the presence of occlusion, and under variations in pose, scale and lighting. Further characterization of IVT indicates that using a small batch size and forgetting factor during update provide better tracking accuracy when face tracks changes in their capture conditions. When conditions change more gradually, IVT benefits from assessing facial quality before updating face models.

show abstract

Section: Methodsmentioning

confidence: 99%

A Comparison of Adaptive Appearance Methods for Tracking Faces in Video Surveillance

Dewan

Granger

Roli

et al. 2013

5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013)

View full text Add to dashboard Cite

show abstract

“…Experiments were conducted on four datasets: FERET [28], AR [29], BANCA [30], and ChokePoint [31]. Figure 2 shows example raw images.…”

Section: Methodsmentioning

confidence: 99%

“…The third dataset is ChokePoint [31], which was recorded under real-world surveillance conditions. It has 16 videos of 29 subjects recorded on four distinct portals 3 .…”

Section: Face Verification With Frontal Facesmentioning

confidence: 99%

On robust biometric identity verification via sparse encoding of faces: Holistic vs local approaches

Wong

Harandi

Sanderson

et al. 2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

Self Cite

View full text Add to dashboard Cite

Abstract-In the field of face recognition, Sparse Representation (SR) has received considerable attention during the past few years. Most of the related literature focuses on holistic descriptors in closed-set identification applications. The underlying assumption in identification is that the gallery always has sufficient samples per subject to linearly reconstruct a query image. Unfortunately, such assumption is easily violated in the more challenging and realistic face verification scenario. A verification algorithm is required to determine if two faces (where one or both have not been seen before) belong to the same person, while explicitly taking into account the possibility of impostor attacks. In this paper, we first discuss why most of the SR literature is not applicable to verification problems. Motivated by the success of bag-of-words methods in the field of object recognition, which describe an image as a set of local patches or interest points, we then propose to tackle the verification problem by encoding each local face patch through SR. The locally encoded sparse vectors are pooled to form regional descriptors, where each descriptor covers a relatively large portion of the face. Experiments in various challenging conditions show that the proposed method achieves high and robust verification performance.

show abstract

“…To compare the performance of the proposed method, video sequences from the Chokepoint dataset [15] are used. They are recorded for a VS scenario, where an array of 3 cameras is placed above different portals (natural choke points in terms of pedestrian traffic) to capture individuals walking through in a natural way.…”

Section: Video Datamentioning

confidence: 99%

“…Simulation results were obtained using video form the Chokepoint dataset [15], where an array of three cameras was placed above several portals to capture individuals walking through.…”

Section: Introductionmentioning

confidence: 99%

Adaptive appearance model tracking for still-to-video face recognition

Dewan

Granger

Marcialis

et al. 2016

Pattern Recognition

View full text Add to dashboard Cite

Systems for still-to-video face recognition (FR) seek to detect the presence of target individuals based on reference facial still images or mug-shots. These systems encounter several challenges in video surveillance applications due to variations in capture conditions (e.g., pose, scale, illumination, blur and expression) and to camera inter-operability. Beyond these issues, few reference stills are available during enrollment to design representative facial models of target individuals. Systems for still-to-video FR must therefore rely on adaptation, multiple face representation, or synthetic generation of reference stills to enhance the intra-class variability of face models. Moreover, many FR systems only match high quality faces captured in video, which further reduces the probability of detecting target individuals. Instead of matching faces captured through segmentation to reference stills, this paper exploits Adaptive Appearance Model Tracking (AAMT) to gradually learn a track-face-model for each individual appearing in the scene. The Sequential Karhunen-Loeve technique is used for online learning of these track-face-models within a particle filter-based face tracker. Meanwhile, these models are matched over successive frames against the reference still images of each target individual enrolled to the system, and then matching scores are accumulated over several frames for robust spatiotemporal recognition. A target individual is recognized if scores accumulated for a track-face-model over a fixed time surpass some decision threshold. The main advantage of AAMT over traditional still-to-video FR systems is the greater diversity of facial representation that may be captured during operations, and this can lead to better discrimination for spatiotemporal recognition. Compared to state-of-the-art adaptive biometric systems, the proposed method selects facial captures to update an individual's face model more reliably because it relies on information from tracking. Simulation results obtained with the Chokepoint video dataset indicate that the proposed method provides a significantly higher level of performance compared state-of-the-art systems when a single reference still per individual is available for matching. This higher level of performance is achieved when the diverse facial appearances that are captured in video through AAMT correspond to that of reference stills.

show abstract

Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition

Cited by 264 publications

References 35 publications

A Comparison of Adaptive Appearance Methods for Tracking Faces in Video Surveillance

A Comparison of Adaptive Appearance Methods for Tracking Faces in Video Surveillance

On robust biometric identity verification via sparse encoding of faces: Holistic vs local approaches

Adaptive appearance model tracking for still-to-video face recognition

Contact Info

Product

Resources

About