<\/span><\/h2>\nAccording to the documentation, Kairos\u2019 Emotion Analysis software measures total attention time, number of glances, blink detection, and attention span, can understand positive, negative and neutral sentiments and detect facial expressions including smiles, frowns, anger, and surprise. It can be integrated using their APIs and SDKs.<\/p>\n
The media uploaded can be an image or a video but the engine appears to be tuned to perform best on video to learn the baseline expressions of the person over time and adjust to compensate for a person\u2019s natural resting expression, which makes the emotion recognition from a single image not as accurate.<\/p>\n
The process involves an HTTP POST request to the API to create a new media object to be processed, to which the response includes the ID of the media uploaded. We need this ID for the second HTTP GET request which, in turn will return the results of the uploaded piece of media, the confidence for the set of emotions, not normalized, in the range from 0 to 100.<\/p>\n
Results :\u2028\r\n\r\n{\u2028 \u00a0 \u00a0\r\n\r\n \"anger\": number, \u2028 \u00a0 \u00a0\r\n\r\n \"disgust\": number, \u2028 \u00a0 \u00a0\r\n\r\n \"fear\": number,\u2028 \u00a0 \u00a0\r\n\r\n \"joy\": number, \u2028 \u00a0 \u00a0\r\n\r\n \"sadness\": number, \u2028 \u00a0\u00a0\r\n\r\n \"surprise\": number\u2028\r\n\r\n}<\/pre>\n<\/span>Evaluation<\/span><\/h2>\n![\"\"](\"https:\/\/www.inovex.de\/wp-content\/uploads\/image14-225x300.jpg\")
<\/p>\n
We\u2019ve evaluated the performance of each of the solutions capturing images with Pepper\u2019s cameras using a) emotion-tagged photos from the Cohn-Kanade (CK+) database and b) real subjects in both cases for the emotions: happy, sad, neutral, surprised, and angry and extracted the predominant emotion detected by each algorithm from the results.<\/p>\n
In order to ensure some variation in the participants, among the 19 subjects there were people from different nationalities, females and males in the age range from 25 to 49 (mean=35, stdev=6). Also, in order to be able to make fair comparisons in every test the conditions were the same in terms of light, position, orientation and distance to the robot.<\/p>\n
The emotion recognition has been tested with posed expressions i.e. in absence of an underlying emotional state as well as with spontaneous reactions, i.e. congruent with an underlying emotional state by means of emotion elicitation techniques. These have been evaluated separately due to recent research that asserts the potential importance of dynamic aspects, such as speed of onset and offset of the expression as well as the degree of irregularity of facial movement for the encoding of spontaneous \u2028versus deliberate emotional facial expressions.<\/p>\n
The image shows the predominant emotion detected by each algorithm as a demo.<\/p>\n
<\/span>Results and conclusion<\/span><\/h2>\nAs well as in other research studies, not all emotions were recognized with the same accuracy. In general, the most easily detected emotion across all of the algortihms is a happy expression, with an extremely accurate detection rate that goes up to 100% in some cases while angry and sad expressions appear to be the hardest to identify. For both of those concrete cases, Pepper\u2019s recognition as well as the self-trained convolutional neural network performed a little bit better than the rest.<\/p>\n
![\"\"](\"https:\/\/www.inovex.de\/wp-content\/uploads\/graph-300x122.png\")
<\/p>\n
The average results graph illustrates the same tendency for each of the solutions: database images could be correctly interpreted without major problems. The reason these expressions are recognized so accurately may lie in the fact that these expressions are „pure“ according to experts, who characterized them as forms of the standard. It is notably more difficult to identify the posed expressions by the subjects and the rates are even lower for spontaneous reactions. However, Pepper\u2019s algorithm seems to be trained well enough to maintain a compelling success rate of almost 60% even with these more subtle or confusing movements.<\/p>\n
Still, Google\u2019s and Microsoft\u2019s services return values which can definitely be relied on, especially when it comes to distinguish a neutral look from an expression and spotting those images where a smile can be seen, which definitely seems to be their strength. An assumption is that these numbers would be even better if the images used had a higher resolution. To match the resolution of Pepper\u2019s algorithm the pictures taken had a resolution of 640 x 480px, which depending on the position and distance to the face might in some cases not be enough to precisely differentiate an emotion and chances are the scores for two of them are too close.<\/p>\n
As it can be appreciated, Kairos\u2019s algorithm performance using still images as media input can not reach such rates at all, not even by far. The way it seems, this has been tuned for best performance using video rather than static images, where some knowledge about the personal characteristics of the individuals can be learned over time. However, the expectations for the execution with still images was definitely higher. \u2028It is intriguing to note that the results obtained with the DCNN implementation do not differ that much from others, in all situations. In this case, the images it was trained on were certainly different from those obtained in the laboratory. This leads to the assumption that these kind of algorithms could perform surprisingly well when they are trained with the same specific data they will be later used with.<\/p>\n
To improve these results in automated emotion recognition, a possibility is to go for a multi-modal approach, i.e. that relies on several signals in addition to the facial expression. These relate to different aspects of the subject\u2019s communication, such as vocal expressions, which among others includes words, utterances and pauses, or physiological cues like heart rate and skin temperature or gestures. The combination of several sources would result in a more reliable output, decreasing the probability of misinterpreting signals.<\/p>\n
Being able to recognize human emotions is just the first step towards emotionally intelligent machines. This can be used to adapt the robot\u2019s behavior and thus improve the quality of human-machine interaction.<\/p>\n
<\/span>References<\/span><\/h2>\n\n- [1] P. Ekman, Universals and Cultural Differences in Facial Expressions of Emotion. University of Nebraska Press,1971.<\/li>\n
- [2] P.Ekman,W.V.Friesen,andJ.C.Hager,\u201cThefacialactioncodingsystem,\u201cinResearchNexus eBook, 2002.<\/li>\n
- [3] T. F. Cootes, G. J. Edwards, and C. J. Taylor, \u201cActive appearance models,\u201c IEEE Trans. Pattern \u2028Analysis and Machine Intelligence, vol. 23, pp. 681\u2013685, June 2001.<\/li>\n
- [4] P. Lucey, J. F. Cohn, T. Kanade, J. M. Saragih, Z. Ambadar, and I. A. Matthews, \u201cThe extended cohn-kanade dataset (CK+): A complete dataset for action unit and emotion-specified expres- sion,\u201c in IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2010, San Francisco, CA, USA, 13-18 June, 2010, pp. 94\u2013101, 2010.<\/li>\n
- [5] \u201cChallenges in representation learning: Facial expression recognition challenge.\u201c http:\/\/www.kaggle.com\/c\/ challengesin-representation-learning-facial-expression-recognitionchallenge. Accessed: 2017-01-23.<\/li>\n
- [6] M. J. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba, \u201cCoding facial expressions with gabor wavelets,\u201c in 3rd International Conference on Face & Gesture Recognition (FG\u201998), April 14- 16, 1998, Nara, Japan, pp. 200\u2013205, 1998.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
Automatic emotion recognition is an emerging area which leverages and combines knowledge from multiple fields such as machine learning, computer vision and signal processing. It has potential applications in many areas including healthcare, robotic assistance, education, market survey and advertising. Another usage of this information is to improve Human Computer Interaction with what can be […]<\/p>\n","protected":false},"author":57,"featured_media":13108,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"ep_exclude_from_search":false,"footnotes":""},"tags":[511,150,140,726],"service":[76,948],"coauthors":[{"id":57,"display_name":"Silvia Santano","user_nicename":"ssantano"}],"class_list":["post-21058","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","tag-artificial-intelligence-2","tag-computer-vision","tag-machine-learning","tag-robotics","service-artificial-intelligence","service-robotics"],"acf":[],"yoast_head":"\n
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