Abstract
Facial expressions are extremely important in the social interaction as they can display the internal emotions and intentions of an individual. Accurately classifying the facial expressions into various categories is the main task in Automatic Facial Expression Recognition (AFER) systems. The existing local based techniques, at times suffer and generate same feature values for different image portions such as edge, corner and flat regions. To address this issue, Radial Mesh Pattern (RMP), a local texture based approach based on the chess game rules is proposed. With reference to the center pixel in a \(5\times 5\) neighborhood, the possible positions of Rook, Bishop and Knight are determined and based on these positions, the features are extracted. In this paper, not only binary weights, but also other weights such as fibonacci, prime, natural, squares, odd and even weights have been utilized for feature extraction. To validate the efficiency of the proposed method, RMP is implemented on six ‘in the lab’ datasets. The performance is measured through recognition accuracy and the results obtained from experiments demonstrate the efficiency of RMP over standard existing methods.
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The datasets used in this work are available in the below links. JAFFE: https://zenodo.org/record/3451524#.YMyv2eFR02x TFEID: https://bml.ym.edu.tw/tfeid/modules/wfdownloads/ KDEF: https://www.kdef.se/download-2/register.html CK+: https://www.pitt.edu/~emotion/ck-spread.htm MUG: https://mug.ee.auth.gr/fed/ OULU-CASIA https://www.oulu.fi/cmvs/node/41316.
References
Aghamaleki JA, Chenarlogh VA (2019) Multi-stream cnn for facial expression recognition in limited training data. Multimedia Tools Appl 78(16):22861–22882
Aifanti N, Papachristou C, Delopoulos A (2010) The mug facial expression database. In: 11th International workshop on image analysis for multimedia interactive services WIAMIS 10, IEEE, pp 1–4
Alenazy WM, Alqahtani AS (2021) Gravitational search algorithm based optimized deep learning model with diverse set of features for facial expression recognition. J Ambient Intell Human Comput 12:1631–1646. https://doi.org/10.1007/s12652-020-02235-0
Ali AM, Zhuang H, Ibrahim AK (2017) An approach for facial expression classification. Int J Biom 9(2):96–112
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720
Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Institute of Brain ScienceTaiwan, Taiwan
Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. Josa A 14(8):1724–1733
Goeleven E, De Raedt R, Leyman L, Verschuere B (2008) The Karolinska directed emotional faces: a validation study. Cognit Emot 22(6):1094–1118
Iqbal MTB, Ryu B, Song G, Kim J, Makhmudkhujaev F, Chae O (2016) Exploring positional ternary pattern (ptp) for conventional facial expression recognition from static images. In: Korea Comput Congress, pp 853–855
Kartheek MN, Prasad MVNK, Bhukya R (2020) Local optimal oriented pattern for person independent facial expression recognition. In: Twelfth international conference on machine vision (ICMV 2019), vol 11433, International Society for Optics and Photonics, p 114330R1-8
Kola DGR, Samayamantula SK (2021) Facial expression recognition using singular values and wavelet-based lgc-hd operator. IET Biometric 10:207–218
Kung H-W, Yi-Han T, Hsu C-T (2015) Dual subspace nonnegative graph embedding for identity-independent expression recognition. IEEE Trans Inf Forensics Secur 10(3):626–639
Lai C-C, Ko C-H (2014) Facial expression recognition based on two-stage features extraction. Optik 125(22):6678–6680
Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010) The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE computer society conference on computer vision and pattern recognition-workshops, IEEE, pp 94–101
Lyons M, Akamatsu Shigeru, Kamachi M, Gyoba J (1998) Coding facial expressions with gabor wavelets. In: Proceedings third IEEE international conference on automatic face and gesture recognition, IEEE, pp 200–205
Maheswari VU, Varaprasad G, Raju SV (2020) Local directional maximum edge patterns for facial expression recognition. J Ambient Intell Humaniz Comput 12:1–9
Makhmudkhujaev F, Abdullah-Al-Wadud M, Iqbal MT, Ryu BB, Chae O (2019) Facial expression recognition with local prominent directional pattern. Signal Process Image Commun 74:1–12
Makhmudkhujaev F, Iqbal MT, Ryu BB, Chae O (2019) Local directional-structural pattern for person-independent facial expression recognition. Turk J Electr Eng Comput Sci 27(1):516–531
Mandal M, Verma M, Mathur S, Kumar VS, Subrahmanyam M, Kranthi KD (2019) Regional adaptive affinitive patterns (radap) with logical operators for facial expression recognition. IET Image Process 13(5):850–861
Martinez A, Du S (2012) A model of the perception of facial expressions of emotion by humans: research overview and perspectives. J Mach Learn Res 13:1589–1608
Murala S, Wu JQM (2013) Local mesh patterns versus local binary patterns: biomedical image indexing and retrieval. IEEE J Biomed Health Inform 18(3):929–938
Reddy PCS, Rao PVP, Reddy PKK, Sridhar M (2019) Motif shape primitives on fibonacci weighted neighborhood pattern for age classification. Soft Comput Signal Process. Springer, Berlin, pp 273–280
Rivera AR, Jorge RC, Chae O (2012) Local directional number pattern for face analysis: Face and expression recognition. IEEE Trans Image Process 22(5):1740–1752
Rivera AR, Jorge RC, Chae O (2015) Local directional texture pattern image descriptor. Pattern Recogn Lett 51:94–100
Ryu B, Rivera AR, Kim K, Chae O (2017) Local directional ternary pattern for facial expression recognition. IEEE Trans Image Process 26(12):6006–6018
Sen D, Datta S, Balasubramanian R (2019) Facial emotion classification using concatenated geometric and textural features. Multimedia Tools Appl 78(8):10287–10323
Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis Comput 27(6):803–816
Sun Z, Hu Z-P, Wang M, Zhao S-H (2017) Individual-free representation-based classification for facial expression recognition. Signal Image Video Process 11(4):597–604
Taskeed J, Hasanul KM, Oksam C (2010) Robust facial expression recognition based on local directional pattern. ETRI J 32(5):784–794
Tian Y-L, Kanade T, Cohn JF (2005) Facial expression analysis. Handbook of face recognition. Springer, Berlin, pp 247–275
Tong Y, Chen R (2019) Local dominant directional symmetrical coding patterns for facial expression recognition. Comput Intell Neurosci 1–13:2019
Tuncer T, Dogan S, Ataman V (2019) A novel and accurate chess pattern for automated texture classification. Phys A Stat Mech Appl 536:122584
Turan C, Lam K-M, He X (2018) Soft locality preserving map (slpm) for facial expression recognition. arXiv preprint. arXiv:1801.03754
Turk M, Alex P (1991) Face recognition using eigenfaces. In: Proceedings of 1991 IEEE computer society conference on computer vision and pattern recognition, pp 586–587
Verma V, Kumar VS, Singh G (2019) Hinet: hybrid inherited feature learning network for facial expression recognition. IEEE Lett Comput Soc 2(4):36–39
Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154
Xie S, Haifeng H, Yongbo W (2019) Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition. Pattern Recogn 92:177–191
Zhao G, Huang X, Taini M, Li SZ, PietikäInen M (2011) Facial expression recognition from near-infrared videos. Image Vis Comput 29(9):607–619
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Kartheek, M.N., Prasad, M.V.N.K. & Bhukya, R. Radial mesh pattern: a handcrafted feature descriptor for facial expression recognition. J Ambient Intell Human Comput 14, 1619–1631 (2023). https://doi.org/10.1007/s12652-021-03384-6
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DOI: https://doi.org/10.1007/s12652-021-03384-6