1. Guillaume-Alexandre Bilodeau
Dynamic Selection of Exemplar-SVMs for Watch-List Screening Through Domain Adaptation
Saman Bashbaghi
Eric Granger
Robert Sabourin
Guillaume-Alexandre Bilodeau
École de technologie supérieure, Université du Québec
December 21, 2016

2. Outline
Introduction
Application, Challenges, Objectives, and Contributions
Ensembles of Exemplar-SVMs Through Domain Adaptation
Design phase (pool generation)
Operational phase (dynamic selection and integration)
Experimental Results
COX-S2V data set
Experimental protocol
Simulation results
Conclusions

3. 1. Introduction Application Area
Generic system for video-based face recognition

4. 1. Introduction Application Area
Video Surveillance applications
detect the presence of target individuals of interest (enrolled to the system) based on their facial model
types of systems (based on facial model):
video-to-video FR (e.g., face re-identification, search and retrieval)
still-to-video FR (e.g., watch-list screening)
individual-specific detection among a restrained list of individuals [Pagano et al., 2012]
Watch-List Screening
recognize target individuals from watch-list images, under semi- and unconstrained environments [Bashbaghi et al., 2014]
face model is defined as either a set of reference samples (stored in a gallery for template matching), or a set of classifier parameters estimated from reference samples (for pattern classification)

5. 1. Introduction Challenges
Limited and Imbalanced Data
design with limited number of target reference face captures and an abundance of non-target references
small number of target inviduals appearing during operations
can estimate of parameters (thresholds, feature subsets, classifiers) with auxiliary data obtained from videos of non- target people in the scene
Face Capture and Classification
different cameras used to capture reference faces (still camera under controlled conditions) and operational faces (IP video camera under semi- or uncontrolled conditions)
nuisance factors – variation in illumination, pose, scale, expression, occlusion, contrast, focus, sharpness, etc.

6. 1. Introduction Objectives
Design robust still-to-video FR systems for watch- list screening approaches:
provide a novel dynamic ensemble selection method for still-to-video FR through individual-specific multi- classifier system
exploit classification systems through domain adaptation with the availability of a single labeled target ROI obtained from a high-quality still reference and unlabeled non-target ROIs captured either from the cohort or low-quality calibration videos
evaluate the performance of different training schemes and dynamic selection of ensembles

7. 1. Introduction Contributions
A robust still-to-video FR can be designed under SSPP using:
Multiple and diverse face representations
uniform patches
Random Subspace Method
provide ensemble diversity and improve robustness to various perturbation factors frequently occur in video surveillance environments
Ensemble of e-SVMs through domain adaptation
discriminate between a single labeled high-quality target ROI and abundant of unlabeled non-target ROIs from calibration videos
Dynamic Classifier Selection
An intuitive approach to provide the best separation w.r.t. non-target samples in the feature space that allows the system to select classifiers properly fit the acquisition conditions

8. 2. Dynamic Ensemble of SVM Classifiers Operational system
Block diagram of the proposed still-to-video FR system using dynamic ensemble of e-SVMs per target individual

9. 2. Dynamic Ensemble of SVM Classifiers Design Phase (Generating a pool)
Multiple Diverse Face Representations
Uniform Patches
local parts of face to provide diversity for ensembles are suitable for partially occluded faces
Feature Extraction Technique
HOG, LPQ
Random Subspace Method
allow for randomly sampling and selection different feature subsets from feature space of input samples
Classification Systems
Exemplar-SVM relies on a single labeled still face from the enrollment domain along with unlabeled faces in calibration videos from the operational domain

10. 2. Dynamic Ensemble of SVM Classifiers Enrollment Phase (Training schemes)
(a) Scheme 1
(b) Scheme 2
(c) Scheme 3
Illustration of e-SVMs in a 2D feature space trained using different classification schemes according. (a) a labeled target still vs labeled non-target still ROIs of Ed, (b) a labeled target still vs unlabeled non-target video ROIs of Od, and (c) a labeled target still vs camera-specific unlabeled non-target video ROIs.

11. 2. Dynamic Ensemble of SVM Classifiers Operational Phase (Dynamic classifier selection)
Illustration of the proposed dynamic classifier selection approach in a 2D feature space

12. 3. Experimental Results Dataset
COX-S2V dataset [Huang et al., 2013]
1000 subjects, where each subject has a high-quality still image, and four lower-quality facial trajectories (each trajectory has 25 faces of 16x20 and 48x60 resolutions)
Example of the reference still of subject ID \#1 and some corresponding videos from the COX-S2V dataset

13. 3. Experimental Results Experimental methodology
Protocol for independent 5 trials
Design phase (randomly select)
20 individuals in the watch-list (20 high-quality stills)
100 unknown person to form UBM (ROIs video capture)
Operational phase (test mode)
100 other unknown persons (ROIs video capture)
ROIs video capture of individuals of interest (one at a time)
Measures: transaction-based analysis (matching performance)
pAUC(20%), AUPROC
Time Complexity: Number of dot products required by the ensemble of e-SVMs to process a probe

14. 3. Experimental Results Transaction-level
Average transaction-level performance and complexity of the proposed system against state-of-the-art s
Average pAUC(20\%) and AUPR performance of different training schemes at transaction-level
Average performance of the proposed system with or without dynamic selection

15. 4. Conclusion
Training schemes 1 is greatly outperformed by schemes 2 and 3, where calibration videos from operational domains are employed for domain adaptation to train e-SVMs
In the scheme 2, videos from all of the cameras are employed to generate an e-SVM pool, while 4 camera- specific e-SVM pools are generated for scheme 3 using videos of each camera
Dynamic selection can improve the performance instead of combining all of classifiers, where it is efficient and does not significantly increase the computational burden

16. Thanks for your attention