Facial Feature Extraction Using Hierarchical MAX(HMAX) Method

Pisal, Akshaya; Sor, Ravindra; Kinage, Kishor S.

doi:10.1109/iccubea.2017.8463755

Cited by 12 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several important factors such as resolution, illumination effects, and intensity of expressions to consider when classifying human facial expressions [20] [21]. They are considered to be important because they are the primary information stored within pixels.…”

Section: Proposed Methodologymentioning

confidence: 99%

Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

Shehu

Browne

Eisenbarth

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Emotion categorization can be the process of identifying different emotions in humans based on their facial expressions. It requires time and sometimes it is hard for human classifiers to agree with each other about an emotion category of a facial expression. However, machine learning classifiers have done well in classifying different emotions and have widely been used in recent years to facilitate the task of emotion categorization. Much research on emotion video databases uses a few frames from when emotion is expressed at peak to classify emotion, which might not give a good classification accuracy when predicting frames where the emotion is less intense. In this paper, using the CK+ emotion dataset as an example, we use more frames to analyze emotion from mid and peak frame images and compared our results to a method using fewer peak frames. Furthermore, we propose an approach based on sequential voting and apply it to more frames of the CK+ database. Our approach resulted in up to 85.9% accuracy for the mid frames and overall accuracy of 96.5% for the CK+ database compared with the accuracy of 73.4% and 93.8% from existing techniques.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

Shehu

Browne

Eisenbarth

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Feature extraction converts pixel data into representations of shapes, movements, colors, textures [13]. Feature extraction is an important stage in the construction of each pattern classification to obtain relevant information from the characteristics of each class so that it can be used in the next stage, namely pattern recognition in an image [14].…”

Section: Feature Extractionmentioning

confidence: 99%

Detection System of Strawberry Ripeness Using K-Means

et al. 2022

View full text Add to dashboard Cite

Strawberry is one type of fruit that is favored by the people of Indonesia. The detection process to identify strawberries can be done by utilizing advances in computer technology, One of them is in the field of digital image processing. In this study, we made a strawberry ripeness detection system using the values of Red, Green and Blue as the reference values, while for identification in determining the type of classification using the K-Means algorithm that uses the Euclidean distance difference as the reference. Based on the results of testing using the K-Means algorithm on 51 strawberry images consisting of ripe, semi ripe and raw fruit yielding an accuracy rate of 82.14%, we also conducted tests other than strawberry images as many as 8 images yielded an accuracy rate of 100%.

show abstract

“…The success rates of these methods are relatively low as compared to deep learningbased studies in recent years. Due to the low success rate, the researcher moves forward to introduce a deep learning model for ESC that has been frequently used in recent years in different fields [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

iMobilAkou: The Role of Machine Listening to Detect Vehicle using Sound Acoustics

Sharif¹,

Hotwani²,

Şeker

et al. 2021

2021 the 5th International Conference on Advances in Artificial Intelligence (ICAAI)

View full text Add to dashboard Cite

Machine Learning can work very well with image recognition, but it is used to recognize audio patterns. Machine listening identifies audio patterns of different entities like the car engine, human speaking, nature sounds, etc. The environmental sound classification plays an important role to encourage citizens to travel smartly within a city without creating unbearable noises. On the other hand, it also promotes the city council to maintain and predict a sustainable sound at rush hour with ins the city. The aim of this early-stage research is to present a methodology that will read the labeled audio files, extract features from them, feed features to a sequential model. Moreover, the model will have the ability to classify these audio files of vehicles based on their input feature(s) and then further categorize them as it either light-weight, medium-weight, heavy-weight, rail-bound or two-wheeled vehicle using the applications of machine listening and deep learning in the field of sound acoustics. Therefore, It will also classify unlabelled test data files on a pre-trained model. This research provides us the base model for the vehicle classification giving both advantages and disadvantages along with the possibility for future extensions. CCS CONCEPTS• Software and its engineering → Integrated and visual development environments; • General and reference → Performance; • Computing methodologies → Concurrent computing methodologies; Massively parallel algorithms.

show abstract

Facial Feature Extraction Using Hierarchical MAX(HMAX) Method

Cited by 12 publications

References 4 publications

Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

Emotion Categorization from Video-Frame Images Using a Novel Sequential Voting Technique

Detection System of Strawberry Ripeness Using K-Means

iMobilAkou: The Role of Machine Listening to Detect Vehicle using Sound Acoustics

Contact Info

Product

Resources

About