Facial Age Classification for Unconstrained Face Images
l Recently, automatic age classification using face information has gained an increasing interest due to its emerging applications such as
Electronic customer relationship management (ECRM)
Security control and surveillance monitoring
l Age classification in the real world is a very challenging task due to the large variation of face appearance.
A variety of human races, genders, facial expression, pose etc.
l Most of existing age classification methods globally modeled faces of age groups (i.e., representing each age group as a single model).
Globally modeling age groups was problematic in an uncontrolled condition
This is because faces of same age group could be significantly different or faces in different age groups could be similar due to the large face appearance variations
l In this research, we propose a new age classification method suitable for dealing with large face appearance variations
We decompose the whole training faces of an age group into a set of face clusters.
Using a few nearest face clusters of each age group, we can avoid the classification degradation due to some undesirable faces
To measure the closeness between a test face and a face cluster, we compute the distance between the test face and the face cluster centroid that represents the representative face appearance.
To further reflect the variation of face appearance, we compute the distance between a test face and the face sample distribution
2. Age Classification Using Local Age Group Modeling
Fig. 1. Overview of the proposed age classification based on local age group modeling.
l Training stage
Feature extraction: captures rich age-related information from face image
Face clustering: groups faces with similar appearance
Construction of local age group model: Each face cluster is converted to a local age group model that enables matching with test face image in the test stage
l Test stage
Selecting nearest local age group models: selects the few local age group models nearest to the test face, in each age group
Computation of similarity scores: obtains overall similarity scores for each age group, by using the few local age group models
Classification: determines the age group label by finding the highest similarity score among all age groups
2.1. Construction of Local Age Group Models from Training Faces
l Step 1: We construct training set of the i-th age group, which consists of feature vectors denoted by .
For feature extraction, we use Local Binary Pattern (LBP) histogram followed by Fisher¡¯s linear discriminant analysis (FLDA).
l Step 2: Using face clustering, we divide into a set of face clusters where each has similar characteristics (in terms of gender, races, facial pose etc).
Because the number of face clusters may vary with different datasets, hierarchical agglomerative clustering (HAC) is used instead of kmeans clustering.
The set of face clusters for the i-th age group is defined as where is the k-th face cluster in .
l Step 3: We construct the local models of .
Compute cluster centroid: to encode representative face appearance of cluster
Compute linear subspace: to encode sample variation information of cluster
2.2. Classification of Test Face by Using Local Age Group Models
l Step 1: Compute the closeness between the test sample and local age group model .
Distance between and cluster centroid of :
Distance between and linear subspace of :
(This is equivalent to the PCA (Principal Component Analysis) residual between and the reconstruction of projected onto the linear subspace )
In (2), corresponds to the reconstruction of (subtracted by the cluster centroid)
l Step 2: Convert the distances and into the similarity scores and .
l Step 3: Compute the combined similarity score by weighted sum of and .
l Step 4: Compute the overall similarity score for each age group, as the average of the R largest similarity scores among all where .
R is too small: the classification may overfit the training data
R is too large: degrade the classification performance by incorporating the training face images that are very different from the test face images
l Step 5: The age group label () of the feature vector is determined by finding the maximum similarity score among G age groups
Fig. 2. Conceptual illustration to show the usefulness of computing closeness between test face and local age group model by considering variation of face appearance.
(a) the distances between the query face and the centroids of two clusters of different age groups. The distances are similar in this example.
(b) the distances between the query face and the linear subspaces of two clusters. The distances are more different, which is useful for classification.
l Used database: Images of Groups database (DB)
This DB contains 28,231 faces from 5,080 Flickr images
Faces often had low-resolution, occlusion, dark glasses, unusual facial expressions etc. (see Fig. 3)
We cropped the face regions and normalized them to 60 (h) x 48 (w) pixel images using eye center positions.
l Construction of dataset 1
Training set: Randomly selected 3,500 (500 per age group) face images
Test set: The remaining 1,050 (150 per age group) face images
This dataset 1 was termed as ¡®Full set¡¯
l Construction of dataset 2
To study age classification on faces with reasonable resolution, we collected 12,080 face images with the eye distance more than 24 pixels
Training set: Randomly selected 2,080 (300 per age group except for the age group ¡®8-12¡¯ where only 280 face images were available) face images
Test set: The remaining 1,050 (100 per age group except for the age group ¡®8-12¡¯ where only 64 face images were available) face images
This dataset 2 was termed as ¡®More than 24 pixels¡¯
Fig. 3. Example face images from the Images of Groups database.
l Comparison with state-of-the-arts
A significant improvement over the baseline can be made about 8%.
This result reveals that the local age modeling could be advantageous over the global age modeling in the presence of huge variation in face appearance.
Our proposed method outperforms the other state-of-the-arts in Table 1.
l Analysis for confusion matrix
Table 2 shows the confusion matrix for the proposed method.
For the age group ¡®0-2¡¯ and ¡®66+¡¯, the proposed method achieves acceptable classification accuracies (around 75%).
Recognizing the other age groups are relatively difficult as these age groups could be confused with both the younger and elder age groups.
Table 2. Comparisons with the baseline and the state-of-the-arts.
<References for comparison methods>
* Baseline: The proposed method in which face clustering was not applied while the other conditions such as feature extraction and classification remained the same as the proposed method.
 A. Gallagher and T. Chen, ¡°Understanding Images of Groups of People,¡± IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
 C. Shan, ¡°Learning Local Features for Age Estimation on Real-life Faces,¡± ACM International Workshop on Multimodal Pervasize Video Analysis (MPVA), 2010.
 J. Ylioinas, A. Hadid, and M. Pietikainen, ¡°Age Classification in Unconstrained Conditions Using LBP Variants,¡± IEEE International Conference on Pattern Recognition, 2012.
 J.-G. Wang, W.-Y. Yau, and H. L. Wang, ¡°Age Categorization via ECOC with Fused Gabor and LBP Features,¡± IEEE Workshop Applic. Comput. Vis. 2012.
Table 2. Confusion matrix using the proposed method (For full set). Row and column mean actual and predicted age groups.
l To reduce the effect of the factors unrelated to age, only a few important local models of each age group affected the age classification.
l Experimental results using a real-world aging database containing images from Flickr verified the effectiveness of the proposed method using local age group modeling.
* Contact Person: Prof. Yong Man Ro (firstname.lastname@example.org)
1. S. H. Lee and Y. M. Ro, ¡°Local Age Group Modeling in Unconstrained Face Images for Facial Age Classification,¡± IEEE Conference on Image Processing, 2014.