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https://github.com/arpandatta011/facial_image_recognition

This project includes the introduction of the facial expression recognition and an investigation on the recent previous researches for extracting the effective and efficient method for facial expression recognition.
https://github.com/arpandatta011/facial_image_recognition

cv2 keras matplotlib numpy pandas tensorflow

Last synced: 2 months ago
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This project includes the introduction of the facial expression recognition and an investigation on the recent previous researches for extracting the effective and efficient method for facial expression recognition.

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# Facial_Image_recognition 🧑‍🦰👩



![](https://imgopt.infoq.com/fit-in/1200x2400/filters:quality(80)/filters:no_upscale()/news/2018/10/EmoPy-Computer-Vision/en/resources/1emotions-1540772527761.png)



Human facial expressions can be easily classified into 7 basic emotions: happy, 

sad, surprise, fear, anger, disgust, and neutral. Our facial emotions are 

expressed through activation of specific sets of facial muscles. These 

sometimes subtle, yet complex, signals in an expression often contain an 

abundant amount of information about our state of mind. Through facial 

emotion recognition, we are able to measure the effects that content and 

services have on the audience/users through an easy and low-cost procedure. 

For example, retailers may use these metrics to evaluate customer interest. 

Healthcare providers can provide better service by using additional information 

about patients' emotional state during treatment. Entertainment producers can 

monitor audience engagement in events to consistently create desired content.

Humans are well-trained in reading the emotions of others, in fact, at just 14 

months old, babies can already tell the difference between happy and sad. But 

can computers do a better job than us in accessing emotional states? To answer 

the question, We designed a deep learning neural network that gives machines 

the ability to make inferences about our emotional states. In other words, we 

give them eyes to see what we can see.



### About the Dataset 



The dataset, used for training the model is from a Kaggle Facial Expression 

ecognition Challenge a few years back (FER2013). The data consists of 48x48 

pixel grayscale images of faces. The faces have been automatically registered 

so that the face is more or less centered and occupies about the same amount of 

space in each image. The task is to categorize each face based on the emotion 

shown in the facial expression in to one of seven categories (0=Angry, 

1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral).

The training set consists of 28,709 examples. The public test set used for the 

leaderboard consists of 3,589 examples. The final test set, which was used to 

determine the winner of the competition, consists of another 3,589 examples. 

Emotion labels in the dataset:

0: -4593 images- Angry

1: -547 images- Disgust

2: -5121 images- Fear

3: -8989 images- Happy

4: -6077 images- Sad

5: -4002 images- Surprise

6: -6198 images- Neutral



### CONCLUSION 💡



The facial expression recognition system presented in this research work 

contributes a resilient face recognition model based on the mapping of 

behavioral characteristics with the physiological biometric characteristics. The 

physiological characteristics of the human face with relevance to various 

expressions such as happiness, sadness, fear, anger, surprise and disgust are 

associated with geometrical structures which restored as base matching emplate 

for the recognition system.The behavioral aspect of this system relates the 

attitude behind different expressions as property base. The property bases are 

alienated as exposed and hidden category in genetic algorithmic genes. The 

gene training set evaluates the expressional uniqueness of individual faces and 

provide a resilient expressional recognition model in the field of biometric 

security. The design of a novel asymmetric cryptosystem based on biometrics 

having features like hierarchical group security eliminates the use of passwords 

and smart cards as opposed to earlier cryptosystems. It requires a special

hardware support like all other biometrics system. This research work promises 

a new direction of research in the field of asymmetric biometric cryptosystems 

which is highly desirable in order to get rid of passwords and smart cards 

completely. Experimental analysis and study show that the hierarchical security 

structures are effective in geometric shape identification for physiological

traits