1. Main Ideas

MotivationThe face systems should be easy for re-training and scalable to accommodate

massive web data.

Archetype Hull

ArchetypesArchetype Hull Model

2. Archetypes

· A geometric model.· Archetypes have a representative power.· Represent all training points as sparse

combinations of a compact set of archetype exemplars.

Archetype Hull Ranking· A scalable graph-based ranking scheme.· Capture query-to-archetype relevance.· Generate rank vectors over the archetype

hull.

4. Face Similarity

Random Initial

Point1

2

1 31

24

3

1

1 5

2

31

6

42

31 5

2

31

24

5

x6

8

2

4

3

5

67 7

4

Archetype Seeking

Archetype Hull Projection

· A sequential seeking strategy towards simplex volume maximization. · Linear space & time complexities in the training dataset size n.

1. Reconstruct an input x using archtypes U:

3. Archetype Hull Ranking

7. Comparison

Face Recognition Task Face Verification Task

2. Form the data-to-archetype affinity matrix:

Build a low-rank data-to-data affinity matrix with the Anchor Graph model.

Graph Based Ranking

· Archetypes Driven Graph Ranking.· Given any query x, the optimal rank vector

over the archetype hull is

where

1. Pre-compute the related matrix for ranking in a linear time O(n).2. Explicitly disclose the relevance between query and archetypes.

Advantages

5. Flowchart of AHR

Blockwise Similarity MeasuresDivide face into blocks and calculate the similarities between block pairs.

· Supervised AHRSuitable when each subject has adequate images.Compute the block-wise similarity by utilizing the identity informationof archetypes.

Face Similarity Measures· Aggregate all blockwise similarity measurements

into a face similarity measure:

· Identical weights for unsupervised AHR. · Learned weights for supervised AHR.

Rank

Face Similarity

Measurement

Archetypes

Block Similarity

Measurements

0.3

2

0.310.37

0.19

0.450.36

Rank

𝐒

𝒛𝟏

𝒛𝟐

𝒓𝟏𝜶

𝒓𝟐𝜶

Feature

Extraction

Blocks

𝐁𝒊𝟏

Archetype Hull

Projection

Archetype Hull

Ranking

Archetype Hull

𝐪𝟐

𝐪𝟏

𝐒𝒌

1.Feature extraction and archetype representation.

2. Rank over the archetype hull to yield the ranking vectors.

3. Aggregate blockwise similarity to obtain face similarity.

6. Choosing ArchetypesType of archetypes

· Dateset: Multi-PIE, Pubfig83· Our method: Unsupervised AHR· Alignment: 5 key-points affine alignment

Dateset: LFW (unsupervised setting)Our method: Supervised AHRAlignment: Rough alignment (LFW-a)

8. LFW Results 9. Conclusions

· The geometric archetype hull model first applied to face modeling.

· The scalable archetype ranking framework using the Anchor Graph.

· Superior recognition and verification accuracy over state-of-the-arts.

· Contact: xy012@ie.cuhk.edu.hk, weiliu@us.ibm.com.0 0.2 0.4 0.6 0.8 1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

False Positive Rate

Tru

e P

ositiv

e R

ate

unsupervised AHR

unsupervised basic AHR

PAF

LARK

· Unsupervised Archetype Hull Ranking (AHR)Compute the block-wise similarity by correlating two archetype representation vectors and (baseline) or two rank vectors and .

and .

Face Recognition via Archetype Hull RankingYuanjun Xiong1, Wei Liu2, Deli Zhao1, and Xiaoou Tang1

1Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong2IBM T.J. Watson Research Center, New York, USA