


Project Mata Kuliah Artificial Intelligence?-?Face Expression Recognition
Dec 29, 2024 pm 05:19 PMShort Explanation
The "Face Expression Recognition" project aims to recognize human facial expressions using the Convolutional Neural Network (CNN) method. The CNN algorithm is applied to analyze visual data such as facial images in grayscale format, which are then classified into seven basic expression categories: happy, sad, angry, surprised, afraid, disgusted, and neutral. This model was trained using the FER2013 dataset and managed to achieve an accuracy of 91.67% after training for 500 epochs.
Project Goals
This "Face Expression Recognition" project is the final project of the Artificial Intelligence course where in this project there are achievements that must be achieved including:
- Developing an artificial intelligence-based facial expression recognition system. This system is expected to be able to identify emotions radiating from facial expressions automatically and accurately.
- Experiment with machine learning algorithms to improve facial expression recognition accuracy. In this project, the CNN algorithm is tested to understand the extent to which this model is able to recognize complex patterns in facial images. This effort also includes optimizing model parameters, adding training data, and using data augmentation methods.
Tech Stack?Used
- Framework: Python uses libraries such as TensorFlow/Keras for CNN implementation.
- Dataset: The dataset used is FER2013 (Facial Expression Recognition 2013), which contains 35,887 grayscale images of faces with dimensions of 48x48 pixels. These images come with labels covering seven basic expression categories.
- Tools:?
- NumPy and Pandas for data manipulation.
- Matplotlib for visualization.
- Haar Cascade for face detection from camera.
Results
- Happy
- Sad
- Angry
- Neutral
- Surprised
- Afraid
- Disgusting
The Problems and How I Deal With?It
The problem of differences in lighting which affects the level of accuracy.?
Lighting variations can affect model accuracy. To overcome this, data normalization is carried out to ensure that the lighting in the image is more uniform so that patterns in facial images can be recognized better.Similar complexity of expressions.
Some expressions, such as “scared” and “surprised,” have similar characteristics that are difficult for the model to differentiate. The solution implemented is to carry out data augmentation such as rotation, zooming, flipping, and contrast changes to increase the generalization ability of the model to new data.Quite limited dataset
The FER2013 dataset, although quite large, does not cover the full range of face variations globally. To enrich the dataset, I used data augmentation techniques and added data from other relevant sources to create a better representation of facial expressions.
Lessons Learned
This project provides deep insight into how artificial intelligence-based systems can be used to recognize facial expressions. The development process shows the importance of:
- Data pre-processing to address lighting issues and improve data quality.
- Experiment training parameters to get the optimal combination, such as setting the number of epochs, learning rate, and batch size.
- Increased diversity of training data through augmentation to improve model performance against real-world data.
By overcoming existing challenges, this project succeeded in building a facial expression recognition model that can be applied to various applications such as human-computer interaction, emotion analysis, and psychological monitoring.
The above is the detailed content of Project Mata Kuliah Artificial Intelligence?-?Face Expression Recognition. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undress AI Tool
Undress images for free

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics

Python's unittest and pytest are two widely used testing frameworks that simplify the writing, organizing and running of automated tests. 1. Both support automatic discovery of test cases and provide a clear test structure: unittest defines tests by inheriting the TestCase class and starting with test\_; pytest is more concise, just need a function starting with test\_. 2. They all have built-in assertion support: unittest provides assertEqual, assertTrue and other methods, while pytest uses an enhanced assert statement to automatically display the failure details. 3. All have mechanisms for handling test preparation and cleaning: un

Python's default parameters are only initialized once when defined. If mutable objects (such as lists or dictionaries) are used as default parameters, unexpected behavior may be caused. For example, when using an empty list as the default parameter, multiple calls to the function will reuse the same list instead of generating a new list each time. Problems caused by this behavior include: 1. Unexpected sharing of data between function calls; 2. The results of subsequent calls are affected by previous calls, increasing the difficulty of debugging; 3. It causes logical errors and is difficult to detect; 4. It is easy to confuse both novice and experienced developers. To avoid problems, the best practice is to set the default value to None and create a new object inside the function, such as using my_list=None instead of my_list=[] and initially in the function

Python's list, dictionary and collection derivation improves code readability and writing efficiency through concise syntax. They are suitable for simplifying iteration and conversion operations, such as replacing multi-line loops with single-line code to implement element transformation or filtering. 1. List comprehensions such as [x2forxinrange(10)] can directly generate square sequences; 2. Dictionary comprehensions such as {x:x2forxinrange(5)} clearly express key-value mapping; 3. Conditional filtering such as [xforxinnumbersifx%2==0] makes the filtering logic more intuitive; 4. Complex conditions can also be embedded, such as combining multi-condition filtering or ternary expressions; but excessive nesting or side-effect operations should be avoided to avoid reducing maintainability. The rational use of derivation can reduce

Python works well with other languages ??and systems in microservice architecture, the key is how each service runs independently and communicates effectively. 1. Using standard APIs and communication protocols (such as HTTP, REST, gRPC), Python builds APIs through frameworks such as Flask and FastAPI, and uses requests or httpx to call other language services; 2. Using message brokers (such as Kafka, RabbitMQ, Redis) to realize asynchronous communication, Python services can publish messages for other language consumers to process, improving system decoupling, scalability and fault tolerance; 3. Expand or embed other language runtimes (such as Jython) through C/C to achieve implementation

PythonisidealfordataanalysisduetoNumPyandPandas.1)NumPyexcelsatnumericalcomputationswithfast,multi-dimensionalarraysandvectorizedoperationslikenp.sqrt().2)PandashandlesstructureddatawithSeriesandDataFrames,supportingtaskslikeloading,cleaning,filterin

To implement a custom iterator, you need to define the __iter__ and __next__ methods in the class. ① The __iter__ method returns the iterator object itself, usually self, to be compatible with iterative environments such as for loops; ② The __next__ method controls the value of each iteration, returns the next element in the sequence, and when there are no more items, StopIteration exception should be thrown; ③ The status must be tracked correctly and the termination conditions must be set to avoid infinite loops; ④ Complex logic such as file line filtering, and pay attention to resource cleaning and memory management; ⑤ For simple logic, you can consider using the generator function yield instead, but you need to choose a suitable method based on the specific scenario.

Dynamic programming (DP) optimizes the solution process by breaking down complex problems into simpler subproblems and storing their results to avoid repeated calculations. There are two main methods: 1. Top-down (memorization): recursively decompose the problem and use cache to store intermediate results; 2. Bottom-up (table): Iteratively build solutions from the basic situation. Suitable for scenarios where maximum/minimum values, optimal solutions or overlapping subproblems are required, such as Fibonacci sequences, backpacking problems, etc. In Python, it can be implemented through decorators or arrays, and attention should be paid to identifying recursive relationships, defining the benchmark situation, and optimizing the complexity of space.

Python's regular expressions provide powerful text processing capabilities through the re module, which can be used to match, extract and replace strings. 1. Use re.search() to find whether there is a specified pattern in the string; 2. re.match() only matches from the beginning of the string, re.fullmatch() needs to match the entire string exactly; 3. re.findall() returns a list of all non-overlapping matches; 4. Special symbols such as \d represents a number, \w represents a word character, \s represents a blank character, *, , ? represents a repeat of 0 or multiple times, 1 or multiple times, 0 or 1 time, respectively; 5. Use brackets to create a capture group to extract information, such as separating username and domain name from email; 6
