[1] FONG B, WESTERINK J. Affective computing in consumer

electronics. IEEE Transactions on Affective Computing, 2012,

3(2): 129 -131.

[2] MELINTE D O, VLADAREANU L. Facial expressions recognition

for human-robot interaction using deep convolutional neural

networks with rectified adam optimizer. Sensors, 2020, 20(8):

Article 2393.

[3] ROSA R L, RODRIGUEZ D Z, BRESSAN G. Music

recommendation system based on user's sentiments extracted from

social networks. IEEE Transactions on Consumer Electronics,

2015, 61(3): 359 -367.

[4] ALAMEDA-PINEDA X, RICCI E, YAN Y, et al. Recognizing

emotions from abstract paintings using non-linear matrix

completion. Proceedings of the 2016 IEEE Conference on

Computer Vision and Pattern Recognition (CVPR'16), 2016, Jun

27 - 30, Las Vegas, NV, USA. Piscataway, NJ, USA: IEEE,

2016: 5240 -5248.

[5] MACHAJDIK J, HANBURY A. Affective image classification

using features inspired by psychology and art theory. Proceedings

of the 18th ACM international conference on Multimedia

(MM'10), 2010, Oct 25 -29, Florence, Italy. New York, NY,

USA: ACM, 2010: 83 -92.

[6] SHINDE S R, SABALE S, KULKARNI S, et al. Experiments on

content based image classification using color feature extraction.

Proceedings of the 2015 International Conference on

Communication, Information & Computing Technology

(ICCICT'15), 2015, Jan 15 - 17, Mumbai, India. Piscataway,

NJ, USA: IEEE, 2015: 1 -6.

[7] YANULEVSKAYA V, VAN GEMERT J C, ROTH K, et al.

Emotional valence categorization using holistic image features.

Proceedings of the 15th IEEE International Conference on Image

Processing, 2008, Oct 12 - 15, San Diego, CA, USA.

Piscataway, NJ, USA: IEEE, 2008: 101 -104.

[8] ZHAO S C, GAO Y, JIANG X L, et al. Exploring principles-of-

art features for image emotion recognition. Proceedings of the 22nd

ACM International Conference on Multimedia (MM'14), 2014,

Nov 3 - 7, Orlando, FL, USA. New York, NY, USA: ACM,

2014: 47 -56.

[9] BORTH D, JI R R, CHEN T, et al. Large-scale visual sentiment

ontology and detectors using adjective noun pairs. Proceedings of

the 21st ACM International Conference on Multimedia (MM'13),

2013, Oct 21 - 25, Barcelona, Spain. New York, NY, USA:

ACM, 2013: 223 -232.

[10] ZHANG J, CHEN M, SUN H, et al. Object semantics sentiment

correlation analysis enhanced image sentiment classification.

Knowledge-Based Systems, 2020, 191(C): Article 105245.

[11] NAG P, SANGSKRITI S, JANNAT M E. A closer look into

Paintings'style using convolutional neural network with transfer

learning. Proceedings of the 11th International Joint Conference on

Computational Intelligence ( IJCCI'19), 2019, Sept 17 - 19,

Vienna, Austria. Lisbon, Portugal: SciTePress ( Science and

Technology Publications, LDA), 2020: 317 -328.

[12] KARNADE A M, DALVI P, KALBANDE D R. Emotion

identification using CNN-based transfer learning. Advanced

Computing Technologies and Applications: Proceedings of the 2nd

International Conference on Advanced Computing Technologies and

Applications (ICACTA´20), 2020, Feb 28 -29, Mumbai, India.

Singapore: Springer, 2020: 337 -344.

[13] JAQUETTI P F, PILLA V J, BORBA G B, et al. VGG FACE

Fine-tuning for classification of facial expression images of emotion.

Proceedings of the XXVII Brazilian Congress in Biomedical

Engineering (CBEB'20), 2020, Oct 26 - 30, VitÓria, Brazil.

2022: 1539 -1546.

[14] ZHANG B, QUAN C Q, REN F J. Study on CNN in the

recognition of emotion in audio and images. Proceedings of the

IEEE/ ACIS 15th International Conference on Computer and

Information Science (ICIS'16), 2016, Jun 26 - 29, Okayama,

Japan. Piscataway, NJ, USA: IEEE, 2016: 1 -5.

[15] RAO T R, XU M, XU D. Learning multi-level deep

representations for image emotion classification. arXiv Preprint,

arXiv: 1611. 07145, 2016.

[16] ZHAO S C, LIN C, XU P F, et al. CycleEmotionGAN: Emotional

semantic consistency preserved cyclegan for adapting image

emotions. Proceedings of the 33rd AAAI Conference on Artificial

Intelligence (AAAI'19), 2019, Jan 27 - Feb 1, Honolulu, HI,

USA. Menlo Park, CA, USA: AAAI, 2019: 2620 -2627.

[17] ZHANG W, HE X Y, LU W Z. Exploring discriminative

representations for image emotion recognition with CNNs. IEEE

Transactions on Multimedia, 2020, 22(2): 515 -523.

[18] YANG J F, SHE D Y, LAI Y K, et al. Retrieving and classifying

affective images via deep metric learning. Proceedings of the 32nd

AAAI Conference on Artificial Intelligence ( AAAI'18), 2018,

Feb 2 - 7, New Orleans, LA, USA. Menlo Park, CA, USA:

AAAI, 2018: 491 -498.

[19] ZHU X G, LI L, ZHANG W G, et al. Dependency exploitation: a

unified CNN-RNN approach for visual emotion recognition.

Proceedings of the 26th International Joint Conference on Artificial

Intelligence ( IJCAI'17 ), 2017, Aug 19 - 25, Melbourne,

Australia. San Francisco, CA, USA: Morgan Kaufmann, 2017:

3595 -3601.

[20] BARRIONUEVO C, IERACHE J, SATTOLO I. Emotion

recognition through facial expressions using supervised learning

with logistic regression. Computer Science: Proceedings of the

26th Argentine Congress ( CACIC'20), 2020, Oct 5 - 9, San

Justo, Buenos Aires, Argentina. CCIS 1409. 2021: 233 -246.

[21] KUMAR C M, SHIVAKUMAR G. A thermal imaging based

classification of affective states using multiclass SVM. Intelligent

Systems Design and Applications: Proceedings of the 18th

International Conference on Intelligent Systems Design and

Applications (ISDA'18), 2018, Dec 6 -8, Vellore, India. AISC

940. 2020: 53 -63.

[22] PATIL S, PATIL Y M. Face expression recognition using SVM and

KNN classifier with HOG features. Applied Computational

Technologies: Proceedings of the 2022 International Conference on

Computing in Engineering & Technology (ICCET'22), 2022, Feb

12 - 13, Lonere, India. SIST 303. Singapore: Springer, 2022:

416 -424.

[23] TIWARI K, PATEL M. Facial expression recognition using

random forest classifier. Proceedings of the International

Conference on Artificial Intelligence: Advances and Applications

2019 ( ICAIAA'19 ), 2019, Jun 28 - 29, Jaipur, India.

Singapore: Springer, 2020: 121 -130.

[24] HUANG R L, DONG H B, YIN G S, et al. Ensembling 3D CNN

framework for video recognition. Proceedings of the 2019

International Joint Conference on Neural Networks (IJCNN'19),

2019, Jul 14 - 19, Budapest, Hungary. Piscataway, NJ, USA:

IEEE, 2019: 1 -7.

[25] VINAY A, SAMPAT P R, BELAVADI S V, et al. Face

recognition using interest points and ensemble of classifiers.

Proceedings of the 4th International Conference on Recent

Advances in Information Technology (RAIT'18), 2018, Mar 15 -

17, Dhanbad, India. Piscataway, NJ, USA: IEEE, 2018: 1 -8.

[26] OLUWAFEMI A G, WANG Z H. Multi-Class weather

classification from still image using said ensemble method.

Proceedings of the 2019 Southern African Universities Power

Engineering Conference/ Robotics and Mechatronics/ Pattern

Recognition Association of South Africa ( SAUPEC/ RobMech/

PRASA'19), 2019, Jan 28 - 30, Bloemfontein, South Africa.

Piscataway, NJ, USA: IEEE, 2019: 135 -140.

[27] YANG H, ZHANG Y S, DING W Z. Multiple heterogeneous P-

DCNNs ensemble with stacking algorithm: A novel recognition

method of space target ISAR images under the condition of small

sample set. IEEE Access, 2020, 8: 75543 -75570.

[28] SU Z B, LIU B, ZHOU X Y, et al. Multidimensional sentiment

recognition of film and television scene images. Journal of

Electronic Imaging. 2021, 30(6): Article 063014.

[29] SU Z B, QIAN Y H, GU Y, et al. Research on emotion space of

film and television scene images based on subjective perception.

The Journal of China Universities of Posts and

Telecommunications, 2019, 26(1): 75 -81.

[30] MACHAJDIK J, HANBURY A. Affective image classification

using features inspired by psychology and art theory. Proceedings

of the 18th ACM International Conference on Multimedia

(MM'10), 2010, Oct 25 -29, Florence, Italy. New York, NY,

USA: ACM, 2010: 83 -92.

[31] ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency tuned

salient region detection. Proceedings of the 2009 IEEE Conference

on Computer Vision and Pattern Recognition (CVPR'09), 2009,

Jun 20 - 25, Miami, FL, USA. Piscataway, NJ, USA: IEEE,

2009: 1597 -1604.

[32] LIU Z, MAO H Z, WU C Y, et al. A convnet for the 2020s.

Proceedings of the 2022 IEEE/ CVF Conference on Computer

Vision and Pattern Recognition (CVPR'22), 2022, Jun 18 -24,

New Orleans, LA, USA. Piscataway, NJ, USA: IEEE, 2022:

11976 -11986.

[33] TAN M X, LE Q V. Efficientnet: Rethinking model scaling for

convolutional neural networks. Proceedings of the 36th

International Conference on Machine Learning (ICML'19), 2019,

Jun 9 -15, Long Beach, CA, USA. PMLR 97. Red Hook, NY,

USA: Curran Associates, 2019: 6105 -6114.

[34] FEI X H, ZHANG Q, LING Q. Vehicle exhaust concentration

estimation based on an improved stacking model. IEEE Access,

2019, 7: 179454 -179463.

[35] YADAV A, VISHWAKARMA D K. A deep learning architecture

of RA-DLNet for visual sentiment analysis. Multimedia Systems,

2020, 26: 431 -451.

[36] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image

is worth 16x16 words: Transformers for image recognition at scale.

Proceedings of the 9th International Conference on Learning

Representations (ICLR'21), 2021, May 3 -7, Vienna, Austria.

2021: 1 -21.