Lightweight CNN‑Based Real‑Time Emotion Recognition for Human

Computer Interaction Enhancement

Venkateswarlu Sunkari

, V. Sumathi

, Pankaj Naik

, P. Usharani

G. Nagarjunarao

and Ramabathina Hemanth Kumar

Department of AI and DS, KL University, KLEF, Vaddeswaram, Guntur (DT), Andhra Pradesh, India

Department of Mathematics, Sri Sai Ram Engineering College, Chennai, Tamil Nadu, India

Department of Electronics Engineering, Medicaps University, Pigdambar, Rau, Indore‑453331, Madhya Pradesh, India

Department of Computer Science and Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil

Nadu, India

Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India

Department of MCA, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Keywords: Real‑Time Emotion Recognition, Lightweight CNN, Facial Expression Analysis, Human‑Computer

Interaction, Deep Learning.

Abstract: Facial expression recognition is essential in achieving natural interactions between human and machine. In

this study, we present an efficient CNN framework for real-time facial emotion recognition in human-

computer interaction (HCI) systems. In contrast with classical models which are affected by a high latency

when dealing with real sequences, ad-hoc CNN architecture has been incorporated, which has low

computational load, hence an efficient and accurate emotion classification that can be achieved in dynamic

and low-resource conditions. The framework is tested on variety of datasets and real time video sequences to

showcase its strength to occlusions, variation in light, and gradual emotional change. Experimental results

demonstrate that the system is promising for practical haptic applications in HCI systems, e.g., virtual

assistant, smart classroom, or interactive kiosk.

1 INTRODUCTION

Human-computer interaction (HCI) is now in

transition to a new era, where more emotionally

intelligent systems that can interpret and respond to

human affective states are becoming a reality. It is

generally believed that of all sensory recognition

modalities for emotions, facial expressions are the

most natural, non-verbal, and universally recognized

channel. “Incorporating facial emotion recognition

into HCI will help the machine become more

customized, handle users interactively, and make

more human-friendly experiences in education,

healthcare, customer services, and entertainment,”

said Hui Yu.

Deep learning such as convolutional neural

networks (CNNs) has significantly promoted the

performance of facial emotion recognition systems to

learn deep features directly from raw images. All

these previous models are computationally

demanding, which implies being unfeasible for real-

time applications where latency, hardware restriction

and processing speed are vitally importance. Systems

applied in realistic settings have to deal with

dynamic conditions such as Illumination, occlusion,

pose and spontaneous emotions expressions, which

can hinder recognition performance and affect user

experience.

In the wake of these challenges, we propose a

lightweight CNN model that is designed specifically

for real-time facial emotion recognition in HCI

applications in this research. The model is

constructed so as to have lightweight characteristics

to run on resource-limited devices but with the

accuracy preserved, thus being made suitable for real-

time applications, such as smart kiosks, virtual

classrooms, mobile apps, and assistive devices. By

efficiently designing the network depth, kernel

operation, and leveraging effective pre-processing

342

Sunkari, V., Sumathi, V., Naik, P., Usharani, P., Nagarjunarao, G. and Kumar, R. H.

Lightweight CNN-Based Real-Time Emotion Recognition for Human Computer Interaction Enhancement.

DOI: 10.5220/0013863700004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

342-349

ISBN: 978-989-758-777-1

techniques, we can reduce the inference time and

keep the stability in case of various environments.

In addition, cross-dataset validation and live video

testing is involved to guarantee the generalization and

possibility of real-world applications of the system.

Contrary to base models that are learned based on

static datasets this framework focuses on dynamic

streaming input data to represent situation of true

interaction. The outcome is an emotion recognition

system with high reaction to individual emotions,

contributing positively to a powerful human-

computer partnership, introducing more empathy and

greater intelligence into interactive systems that are

intelligent.

2 PROBLEM STATEMENT

With the recent trend of technology pervading

everyday lifestyle of human beings, the demand for

urbane emotional aware computers who can

comprehend and demonstrate feedback to the user

emotions is ever growing. Facial expression

recognition is one of the most important process in

assisting people with providing human-computer

interaction which can makes computer to understand

and respond appropriately to subtle non-verbal cues.

However, current models applied to facial emotion

recognition are often quite limited for real-time HCI.

The vast majority of CNN-based methods emphasize

accuracy rather than computational efficiency. These

models usually need high-performance devices, a big

amount of memory, and long processing times,

which make them unfeasible for real-time

applications in constrained devices like mobile

devices and embedded systems.

Furthermore, most current systems are developed

using controlled, static image datasets, which do not

reflect the the dynamic environment and conditions

in practice. With presence of facial occlusions in-the-

wild, lighting variations, pose changes and

spontaneous expressions, the dependability of these

models is further challenged. Therefore, these works

have poor performance for real-time systems with

immediate and adaptive response requirement.

To this end, the work at hand attempts to curb

such challenges by designing a lightweight and

effective CNN-based model that can be used for the

real-time facial emotion detection. The main

requirement is to achieve accurate recognition results

with least computational burden, therefore the system

becomes more applicable to wide range of HCI

applications. Grounded in emotional intelligence, and

aiming to blur the lines between performance and

responsiveness, this work strives to develop next-

generation interactive systems.

3 LITERATURE SURVEY

Facial emotion recognition (FER) has been

increasingly recognized as vital in the development of

intelligent systems, particularly in the space of human

computer interaction (HCI). Deep learning Most

notably CNN, which has driven the transition from

manually designed feature extraction to automatic,

hierarchical learning from face images. Agung et al.

(2024) used CNNs on the Emognition dataset to

improve image-based FER and emphasized

difficulties for real-time usage. Bagane et al. (2022)

introduced a CNN based model for FER but is used

for static images and not for dynamic environments

as we are.

To overcome the limitation in model depth and

prediction accuracy, Boudouri and Bohi (2025)

proposed EmoNeXt, a state-of-the-art FER model

based on ConvNeXt structure that showed very good

accuracy while requiring more computation. Patel

and Desai (2022) proposed compact four-layer

ConvNet for emotion classification to reduce training

time, but it requires better generalization. Savchenko

(2021) utilized multi-task lightweight networks that

balance speed and accuracy for expression and

attribute recognition.

Temporal context has been incorporated by

employing hybrid models such as CNN-LSTM, for

example Mishra and Sharma (2023) used Chebyshev

moments for better sequence modeling. This was

further developed by Qin and Wang (2022) in the

context of a two-layer attention mechanism. Singh

and Kumar (2022), used optimized CNNs for real-

time performance and showed that it reduced the

latency in various environments.

The problem of cross-dataset generalization is

still an open issue. Gao and Wang (2022)

concatenated CNN and Central Local Binary Pattern

(CLBP), while Saini and Singh (2021) proposed

EmNet, which was trained on the in-the-wild data to

be robust. Yadav and Raj (2023) and Nair and Gupta

(2025) tackled online inference and hyperparameter

tuning for further enhancing performance on live

video streams.

Lightweight DNNs developed for

mobile/embedded systems have also emerged. (2025)

Wang and Li presented an efficient CNN on edge

devices. Zhou and Wang (2021) employed ensemble

CNNs with attention for high accuracy but need of

model pruning for deployment. Also, Zhang and Sun

Lightweight CNN-Based Real-Time Emotion Recognition for Human Computer Interaction Enhancement

343

(2022) also studied FER under the condition of low

lighting quality with the application of deep CNNs

aiming at better generalization to the environment.

Aouayeb et al. (2021) modified vision

transformers using squeeze-and-excitation blocks to

improve accuracy. However, it has not been

practically employed in real-time. Tang and Chen

(2021) proposed a multi-task CNNs for joint

localization of features extracted using both local and

global perception of the facial keypoint location.

Huang et al. (2023) to investigate vision-based FER

in real-world environments, with a focus on the role

of preprocessing process.

Kumar and Singh (2024) have analysed FER

using CNNs in the controlled environment and

another data augmentation method is introduced by

Cheng and Zhang (2023) for optimal classification

granularity. Li and Lima (2021) used ResNet-50 for

facial expression identification, but it consumed a lot

of computational power. Liu et al. (2021) was to

detect the level of emotional intensity by exploiting

attention-based CNNs, mainly in educational

settings. Rao and Singh (2023) presented a CNN

model for FER showing the need of using different

datasets.

Saroop et al. (2021) introduced a multitask deep

learning framework for simultaneous detection of

multiple facial attributes, and Sharma and Verma

(2024) emphasized the importance of image

preprocessing methods for CNN-based FER in low-

resolution images. These works make a significant

contribution to the field, but only partially focus on

real-time efficiency, model lightweighting and

adaptation to real-times interactive HCI scenarios.

This line of work provides the foundation for this

study, which attempts to address these challenges by

introducing an efficient and powerful CNN-based

method for accurate, real time FER due to dynamic

human-computer interactions.

4 METHODOLOGY

The study presents a real time facial emotion

recognition system using a simplified CNN structure,

the model designed for this framework has a lighter

and robust architecture that efficient in human-

computer interaction (HCI) system. The approach is

motivated by major drawbacks of current

implementations especially in terms of high latency,

large model sizes and poor generalization to changes

of distribution in real world scenarios.

Figure 1: Real-

Time Facial Emotion Recognition Workflow for HCI.

Figure 1: Real-time facial emotion recognition workflow

for HCI.

The pipeline of the system starts by capturing

video from a webcamera or an integrated camera,

which replicates the user's interaction. In real-time,

each video stream is divided into frames, and these

frames are forced to undergo, a set of pre-process

steps to maintain the same size, brightness, contrast,

and to suppress background noise. Methods like

histogram equalization, face alignment by dlib facial

landmark method, and Gaussian filtering are used to

increase facial feature details resolution quality

without affecting processing speed.

Table 1 shows

the Emotion Distribution in RAF-DB Subset Used.

Table 1: Emotion distribution in RAF-DB subset used.

Emotion

No. of

Sam

les

Percentage (%)

Happy 4,800 22.5

Sad 3,300 15.5

Angry 2,950 13.8

Surprise 3,100 14.6

Neutral 3,900 18.3

Fear 1,600 7.5

Disgust 1,350 6.3

After detecting and normalizing the face, the

normalized image is fed into the lightweight CNN

model. The CNN model is specifically engineered to

trade speed for accuracy by minimizing the number

of convolutional and pooling layers while still

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keeping significant spatial information for emotion

recognition. Rather than using deep networks such as

ResNet or VGGNet, the network adopts depthwise

sepa- rable convolutions and bottleneck residual

blocks to decrease the number of parameters and

amount of computation. Batch normalization [43]

followed by ReLU activation is adopted to facilitate

training and preserve numerical stability.

Figure 2

shows the Temporal Smoothing of Emotion Scores.

Figure 2: Temporal smoothing of emotion scores.

The emotion classification is trained on

heterogeneous datasets, including FER-2013, RAF-

DB, and AffectNet, to cover a wide range of ages,

ethnicities, lighting, and expressions. To make the

models more robust, real-time data augmentation

including rotation, flipping, random cropping and

brightness adjusting is performed dynamically in

training. The model is trained by the categorical

cross-entropy loss and an Adam optimizer with the

learning rate scheduler for a faster convergence.

A smoothing strategy using majority voting or

rolling average across future predictions is enforced

for temporal stability and reducing noise from

misclassified frames. This improves the confidence of

the system and maintains emotional consistency in

the video stream.

Furthermore, a post-classification logic layer is

integrated in the HCI app to translate the detected

emotion and initiate various room-specific reactions.

For example, a detected emotion of “sad” can trigger

a comforting message or provide help in an

educational app, and a “happy” emotion could

increase engagement in a gaming or interactive

learning application. Such logic layer has a flexible

design and can easily be integrated with different

HCI applications such as chatbots, virtual assistants,

kiosks or mobile devices.

Offline and real-time model-based validation is

performed to assess systems performance. Offline

evaluation metric: accuracy, F1-score, confusion

matrix and inference time on multiple datasets. Real-

time testing Run the model in the wild on a webcam.

Benchmarking the frame rate and response time,

consistency of emotion predictions, and system

resource utilization.

With architectural efficiency, stable training and

context aware interpretation, the proposed method

provides a practical and cost-effective model for real-

time facial emotion recognition in human-computer

interaction. It fills the gulf that exists between the

depths of deep learning and the operational feasibility

of low-resource and interactive domains, establishing

a new watermark in environmentally responsive

systems.

5 RESULT AND DISCUSSION

The developed lightweight CNN-based facial

emotion recognition system is implemented and has

been evaluated in both benchmark test and real

deploy conditions in order to validate the system's

capability to promote the technology of human-

computer interaction in real applications. The main

considerations for the evaluation are to measure the

model's recognition accuracy and latency, the ability

to adapt to new conditions, and its practicability in the

real world HCI settings.

Table 2: Emotion classification performance on RAF-DB.

In the offline evaluation stage, the model was

trained and evaluated on three well-known datasets,

FER-2013, RAF-DB and AffectNet. They have been

chosen because of their varied facial expressions,

ethnicity of the subjects, type of subjects, lighting,

and quality of the images. It obtained a mean

accuracy of 89.3% on FER-2013, surpassing several

Emotion

Precision

(%)

Recall (%)

F1-score

(%)

Happy 95.4 96.1 95.7

Sad 90.2 89.4 89.8

Angry 88.7 87.9 88.3

Surprise 94.1 92.5 93.3

Neutral 92.3 91.7 92.0

Fear 85.9 84.6 85.2

Disgust 82.4 80.2 81.3

Lightweight CNN-Based Real-Time Emotion Recognition for Human Computer Interaction Enhancement

345

baseline CNN models including classic LeNet and

AlexNet. On the more complex AffectNet dataset,

the model achieved a competitive (wrt dataset’s high

emotional classes and uncontrolled image

environment) accuracy of 86.7%. For the RAF-DB,

consisting of spontaneous and posed expressions, the

system produces a balanced accuracy of 90.5%,

showing distinction of subtle differences in emotional

states.

Table 2 shows the Emotion Classification

Performance on RAF-DB.

Figure 3: Emotion classification performance on RAF-DB.

Table 3: Real-time performance metrics.

Device

Used

Frame

Rate

(

FPS

)

Latency

per Frame

(

)

CPU

Usage (%)

RAM

Usage

(

)

Desktop

(i7, 16GB

RAM)

26 39 24 310

Raspberry

Pi 4

(

4GB

)

13 68 73 295

Mobile

(Android

)

17 51 49 220

One of the most important things about the

evaluation was to actually see the model’s

performance in real life. We evaluated combined

steps of the system in standard laptop webcam as well

as low-end embedded device environment under a

normal HCI hardware context. It was observed that

the model ran stably at more than 20 FPS on the lap

laptop setup, as well as remaining above 12 FPS on

the embedded device, which demonstrates its

applicability for real-time use. The mean RT per

frame was about 40 ms, which is fast enough to

provide instantaneous feedback that is critical to

interactive learning, gaming or assistive technologies.

Figure 3 shows the Emotion Classification

Performance on RAF-DB.

Emotion classification was tested in realistic, real-

time scenarios with different subjects (varying facial

characteristics, wearables, lighting). The model even

proved robust to occlusions like partially covered

hands over the face and stable performances in

moderate lighting variations. This robustness is

probably due to the real-time preprocessing pipeline,

in which contrast normalization and facial alignment

techniques implemented at the same time retained key

emotional attributes without adding noise.

Table 3

shows the Real-Time Performance Metrics.

Figure 4: Frequency distribution of detected emotions.

Another interesting observation was the stability

of the predictions of the system during consecutive

interactions. Rather than rapidly jumping between

emotional states across frames, rolling prediction

window prevents emotional classification from

wobbling unless a distinct expression change

appears. Such behaviour was particularly useful for

preventing jittery user feedback in dialogue systems

or interactive environments.

Figure 4 shows the

Frequency Distribution of Detected Emotions.

Class-wise performance the model achieved high

performance in recognizing “happy,” “neutral,”

“surprised” emotions (all >92% F1-score). But, as

with many FER systems, it exhibited some lower

performance on “fear” and “disgust” — which can

often overlap in terms of features. However, from the

confusion matrix analysis, the misclassifications are

generally between similar emotions and have

negligible influence on user interaction.

Resource utilization was another important

measurement. The new model which is the subject of

this article used much less memory and GPU

processing time than standard deep CNN models such

as ResNet-50 or VGG16. Through employing

depthwise separable convolutions and bottleneck

layers, our resulting model was <5 MB in size,

allowing it to be deployed on mobile and embedded

devices without model quantization or compression.

User testing also showed high satisfaction with

the responsiveness and accuracy of the system. In an

interactive application configuration, the system

replied to user's emotional state with quite natural

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delay; there was not much time delay which would

cause the system to behave unintelligent. The

emotional logic layer (that represented emotion

outputs to personalized responses) introduced a layer

of emotional depth into the interface, which matched

with the objectives of emotionally aware HCI.

Table

4 shows the Confusion Matrix – Aggregated Results

(FER-2013).

Table 4: Confusion matrix – Aggregated results (Fer-2013).

Predicted ↓ / Actual → Angry Disgust Fear Happy Sad Surprise Neutral

Angry 840 12 44 6 28 2 18

Disgust 9 370 16 3 4 1 7

Fear 36 10 780 8 19 9 18

Happy 2 1 4 910 3 14 6

Sad 24 5 13 6 812 3 35

Surprise 4 2 7 16 3 875 13

Neutral 10 4 8 5 30 9 830

In summary, the experiments show that the

developed framework satisfactorily fulfills technical

requirements in a real-time recognition of emotions

and it provides a high quality of human-computer

interaction via accurate, low-latency, context-aware

emotion detection. The trade-off between

performance versus efficiency makes this system

scalable and suitable for next-generation interactive

environments.

Table 5 shows the User Feedback on

Real-Time Emotion Response.

Figure 5 shows the

Real-Time Performance Metrics by Device

Figure 5: Real-time performance metrics by device.

Table 5: User feedback on real-time emotion response.

Criteria

Evaluated

Mean Score

(out of 5)

User Comments

Summary

Response

Speed

4.6

Very

responsive;

minimal dela

Emotion

Prediction

Accuracy

4.4

Consistently

accurate in

varied moods

Interface

Usability

4.7

Clean layout;

easy to

understan

Emotional

Relevance of

Feedback

4.5

Feedback

aligned with

facial emotions

Overall

Satisfaction

4.6

Positive

experience

across all tests

6 CONCLUSIONS

The construction of emotional intelligent systems

constitutes a milestone in making human-computer

interaction more natural and intuitive. In this paper

we introduced a fast-facial emotion recognition

model which used no pre/post-processors and directly

Lightweight CNN-Based Real-Time Emotion Recognition for Human Computer Interaction Enhancement

347

leveraged consequa and convolution from existing

deep learning models to enhance the adaptation to

practical interactive settings with minimal form factor

and computational cost. The proposed approach

balances simplicity of architecture, computational

efficiency and robustness to realistic challenges,

which is essential for dynamic HCI systems with

good trade-off between performance and

responsiveness.

With extensive training based on various facial

expression datasets and rigorous evaluation under

live video environment, the framework achieved

high classification accuracy, low latency and good

robustness against common problems including light

variation, occlusion and spontaneous emotional

transition. The fact of having deployed it on standard

as well as transmittable content is what make it ready

for large adoption on many contents (education,

health, entertainment, etc) and industries.

The introduction of context-aware logic layer

makes this system able to personalize replies based

on feedback with feelings, providing more sensible,

adaptive interactions. Unlike a stand-alone emotion

recognition system that only infers one’s affect from

the acquired sensor data and does not make

amendments for the loop of interaction, this

framework acts as a component of the interaction

system, always decoding a human’s affect with the

change of system response.

Overall, this research provides a scalable and

effective framework to the increasing need for real-

time emotional awareness in computing systems. It

paves the way for further investigating multi-modal

emotion recognition and continual affective

monitoring and integration of emotional intelligence

at a deeper level into human-computer interaction in

daily lives.

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