Face Classification Using Discriminative Features and Classifier Combination

A mesura que la tecnologia evoluciona, apareixen noves aplicacions en el mon de la classificacio facial. En el reconeixement de patrons, normalment veiem les cares com a punts en un espai de alta dimensionalitat definit pels valors dels seus pixels. Aquesta aproximacio pateix diversos problemes: el fenomen de la la malediccio de la dimensionalitat, la presencia doclusions parcials o canvis locals en la il·luminacio. Tradicionalment, nomes les caracteristiques internes de les imatges facials shan utilitzat per a classificar, on normalment es fa una extraccio de caracteristiques. Les tecniques dextraccio de caracteristiques permeten reduir la influencia dels problemes mencionats, reduint tambe el soroll inherent de les imatges naturals alhora que es poden aprendre caracteristiques invariants de les imatges facials. En la primera part daquesta tesi presentem alguns metodes dextraccio de caracteristiques classics: Analisi de Components Principals (PCA), Analisi de Components Independents (ICA), Factoritzacio No Negativa de Matrius (NMF), i lAnalisi Discriminant de Fisher (FLD), totes elles fent alguna mena dassumpcio en les dades a classificar. La principal contribucio daquest treball es una nova familia de tecniques dextraccio de caracteristiques usant el algorisme del Adaboost. El nostre metode no fa cap assumpcio en les dades a classificar, i construeix de forma incremental la matriu de projeccio tenint en compte els exemples mes dificils Per altra banda, en la segon apart de la tesi explorem el rol de les caracteristiques externes en el proces de classificacio facial, i presentem un nou metode per extreure un conjunt alineat de caracteristiques a partir de la informacio externa que poden ser combinades amb les tecniques classiques millorant els resultats globals de classificacio. _______________________________________________________________ As technology evolves, new applications dealing with face classification appear. In pattern recognition, faces are usually seen as points in a high dimensional spaces defined by their pixel values. This approach must deal with several problems such as: the curse of dimensionality, the presence of partial occlusions or local changes in the illumination. Traditionally, only the internal features of face images have been used for classification purposes, where usually a feature extraction step is performed. Feature extraction techniques allow to reduce the influence of the problems mentioned, reducing also the noise inherent from natural images and learning invariant characteristics from face images. In the first part of this thesis some internal feature extraction methods are presented: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Non Negative Matrix Factorization (NMF), and Fisher Linear Discriminant Analysis (FLD), all of them making some kind of the assumption on the data to classify. The main contribution of our work is a non parametric feature extraction family of techniques using the Adaboost algorithm. Our method makes no assumptions on the data to classify, and incrementally builds the projection matrix taking into account the most difficult samples. On the other hand, in the second part of this thesis we also explore the role of external features in face classification purposes, and present a method for extracting an aligned feature set from external face information that can be combined with the classic internal features improving the global performance of the face classification task.