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Neural Computing and Applications

Erschienen in:

01.06.2012 | ICONIP2010

Machine learning approach for face image retrieval

verfasst von: Dianhui H. Wang, Paul Conilione

Erschienen in: Neural Computing and Applications | Ausgabe 4/2012

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Abstract

Face image retrieval (FIR) is useful to many domain applications, such as helping police to catch criminals or managing householders. However, little research has been done that uses individual face features for image comparison and retrieval. This paper aims to develop a machine learning approach for face image retrieval based on the local face features of the eyes, nose, and mouth. Neural networks are used to localise facial features, and to implement a learning pseudo metric (LPM) to filter out irrelevant images for retrieval efficiently based on semantic information. Our FIR system performs below average given traditional performance measures, but inspecting actual retrieved images it shows strong promise. It is observed that the LPM semantic filtering method was found to reduce the database size by up to 50% without a significant reduction in retrieval performance.

Vorheriger Artikel Adaptive cascade of boosted ensembles for face detection in concept drift

Nächster Artikel Decision-based filter based on SVM and evidence theory for image noise removal

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Any distance measure can be used in this stage.

Known as stratified k-fold cross-validation.

The 13 images consisted of neutral expression, smiling, angry, screaming, neural expression with right light, neural expression with left light, neural expression with both lights on, sunglasses, sunglasses with right light on, sunglasses with left light on, scarf, scarf with right light on, and scarf with left light on.

Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40:1–60CrossRef

Fauzi MFA, Lewis PH (2009) Query by low-quality image. Image Vis Comput 27:713–724CrossRef

Liu W, Tang X, Liu J (2007) Bayesian tensor inference for sketch-based facial photo hallucination. In: Veloso MM (ed) Proceeding international joint conference of artifical intelligence. Morgan Kaufmann Publishers Inc., San Francisco, pp 2141–2146

Sridharan K, Nayak S, Chikkerur S, Govindaraju V (2005) A probabilistic approach to semantic face retrieval system. In: Kanade T, Jain A, Ratha NK (eds) Audio- and video-based biometric person authentication, vol 3546 of lecture notes in computer science. Springer, Berlin, pp 977–986

Ai H, Liang L, Xiao X, Xu G (2001) Face indexing and retrieval in personal digital album. In: Proceedings of 2nd IEEE Pacific Rim conference multimedia, vol 2195, Springer-Verlag, London, UK, pp 48–54

Gao Y, Qi Y (2005) Robust visual similarity retrieval in single model face databases. Pattern Recognit 38:1009–1020CrossRef

Wu B, Ai H, Huang C (2004) Facial image retrieval based on demographic classification. In: Proceedings of 17th International Conference of Pattern Recognition, vol 3. IEEE Computer Society, Washington, pp 914–917

Santini S, Jain R (1999) Similarity measures. IEEE Trans Pattern Anal Mach Intell 21:871–883CrossRef

Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal Mach sIntell 22:1349–1380CrossRef

10.

Liu Y, Zhang D, Lu G, Ma W-Y (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recognit 40:262–282MATHCrossRef

11.

Wang DH, Ma XH, Kim YS (2005) Learning pseudo metric for intelligent multimedia data classification and retrieval. J Intell Manuf 16:575–586CrossRef

12.

Turk M, Pentland A (1991) Eigenfaces for recognition. Cogn Neurosci 3:71–86CrossRef

13.

Belhumeur PN, Hespanha J, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720CrossRef

14.

Cai D, He X, Han J, Zhang H-J (2006) Orthogonal laplacianfaces for face recognition. IEEE Trans Image Process 15:3608–3614CrossRef

15.

He X, Cai D, Han J (2008) Learning a maximum margin subspace for image retrieval. IEEE Trans Knowl Data Eng 20:189–201CrossRef

16.

Yang M-H, Kriegman DJ, Ahuja N (2002) Detecting faces in images: a survey. IEEE Trans Pattern Anal Mach Intell 24:34–58CrossRef

17.

Zhou H, Yuan Y, Sadka AH (2008) Application of semantic features in face recognition. Pattern Recogn 41:3251–3256MATHCrossRef

18.

Conilione P, Wang DH (2011) Automatic localization and annotation of facial features using machine learning techniques. Soft Comput 15:1231–1245

19.

Wang DH, Ma XH (2005) A hybrid image retrieval system with user’s relevance feedback using neurocomputing. Informatica 29:271–279

20.

Lu Y, Guo H, Feldkamp L, Robust neural learning from unbalanced data samples. In: Proceedings of IEEE International Joint Conferences of Neural Networks, vol 3. Anchorage, Alaska, USA, pp 1816–1821

21.

Wang DH, Kim Y-S, Park SC, Lee CS, Han YK (2007) Learning based neural similarity metrics for multimedia data mining. Soft Comput 11:335–340CrossRef

22.

Martinez AM, Benavente R (1998) The AR face database, technical report 24, Computer Visual Center

23.

Mller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533CrossRef

Titel: Machine learning approach for face image retrieval
verfasst von: Dianhui H. Wang
Paul Conilione
Publikationsdatum: 01.06.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 4/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0665-8

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